Effect of Mixing Time on the Lubricating Properties of Magnesium Stearate and the Final Characteristics of the Compressed Tablets

The effect of mixing time on hardness, disintegration time and ejection force in tablettlng of magnesium stearate and lactose granules was studied. The hardness of the tablets decreased with an increase in mixing time of the blends, as previously reported. A semilogarithmic plot of the hardness versus mixing time gave a straight line having a turning point. At the early phase of mixing the hardness was decreasing with a large first-order rate and then continued to decrease with another small first-order rate. The change in disintegration time or ejection force versus mixing time was basically the same as that in the hardness. This type of plot was applicable to the mixing magnesium stearate with not only granular but also powdered materials.     

Development of a current detection type cable fault locator

A cable fault location instrument based on the detection of traveling wave currents was developed. It is different from the conventional pulse radar method based on voltage detection. Measurement is automated, and the distance to the fault point is displayed by merely pushing a switch. In a branched or crossbonded line, the fault point can be located without interference from the branched or the crossbonded point. Tests performed in both real and simulated lines confirmed the practical utility of this instrument     

Sialon formation by Si+ and N2 + ion implantation into sapphire

Single-crystal alumina was implanted firstly with 400 keV Si⁺ and subsequently with N₂ ⁺ ions and then annealed at 1673 K in an No atmosphre. The implanted layers were characterized by means of X-ray diffraction, Rutherford backscattering-channelling of 2 MeV He⁺ ions, and the resonance nuclear reaction¹⁵N(p,)¹²C. The annealing of sapphire implanted at ambient temperature resulted in the formation of-sialon, a solid solution of-silicon nitride and alumina in the subsurface layer, while implantation at 100 K resulted in the formation of aluminium oxynitride in the surface layer. In the latter case, the implanted silicon atoms were believed not to react vxi1h the implanted nitrogen atoms but with the substrate oxygen atoms. These crystalline precipitates were found to have epitaxial relations with the sapphire substrate.     

Fabrication of Nonadditive Mica Ceramics by Hot Isostatic Processing

Powder compacts of synthetic mica (fluorphlogopite) encapsulated in a boro–silicate glass tube were isostatically hot–pressed in a Roy–Tuttle–type pressure vessel. Synthetic mica was sintered to a density of 2.60 g/cm³ (91.2% of theoretical density) without additives under 98 MPa of water at 800°C for 45 min.     

The influence of catalyst-supporting methods on electrochemical activity and the resultant stability of air electrodes activated with iron phthalocyanine

To improve the performance of air electrodes, the dependence of iron phthalocyanine (FePc) catalytic effects on preparation methods was examined. The methods used were mixture (Electrode 1), impregnation (Electrode 2) and direct synthesis (Electrode 3). Electrodes 2 and 3 showed higher potentials during cathodic polarization up to 10 mA cm^–2 than Electrode 1. The rate of chemical destruction of H₂O₂ decreased in the order Electrode 3 > Electrode 2 > Electrode 1. Electrode 3 showed the smallest potential drop for a discharge at 10 mA cm^–2, 0.09 V after 50 h. However, the potential of Electrode 2 decreased with discharge, becoming 0.09 V lower than that of Electrode 3 after a 50 h discharge at 10mA cm^–2. Once the potential drop occurred, the potential was not recovered by resting or by drying the electrode. The potential drop may be caused by deactivation of FePc. One possible reason for such deactivation is the presence of H₂SO₄, which remained on the electrode after impregnation of the FePc-H₂SO₄ solution.     

Integrative Bayesian model on two opposite types of sensory adaptation

Adaptation is a fundamental property of human perception. Recently, it was found that there are two opposite types of adaptation to repetitive stimuli with a temporal difference. In this article, we construct an integrative model of adaptation. We model the perception as a Bayesian inference, and represent the two types of adaptation as changes in the likelihood function and the prior distribution in the Bayesian inference. We examine our model analytically and show how the types of adaptation depend on model parameters.     

Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer

A single-celled amoeboid organism, the true slime mold Physarum polycephalum, exhibits rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. The authors previously created a biocomputer that incorporates the organism as a computing substrate to search for solutions to combinatorial optimization problems. With the assistance of optical feedback to implement a recurrent neural network model, the organism changes its shape by alternately growing and withdrawing its photosensitive branches so that its body area can be maximized and the risk of being illuminated can be minimized. In this way, the organism succeeded in finding the optimal solution to the four-city traveling salesman problem with a high probability. However, it remains unclear how the organism collects, stores, and compares information on light stimuli using the oscillatory dynamics. To study these points, we formulate an ordinary differential equation model of the amoeba-based neurocomputer, considering the organism as a network of oscillators that compete for a fixed amount of intracellular resource. The model, called the “Resource-Competing Oscillator Network (RCON) model,” reproduces well the organism’s experimentally observed behavior, as it generates a number of spatiotemporal oscillation modes by keeping the total sum of the resource constant. Designing the feedback rule properly, the RCON model comes to face a problem of optimizing the allocation of the resource to its nodes. In the problem-solving process, “greedy” nodes having the highest competitiveness are supposed to take more resource out of other nodes. However, the resource allocation pattern attained by the greedy nodes cannot always achieve a “socially optimal” state in terms of the public cost. We prepare four test problems including a tricky one in which the greedy pattern becomes “socially unfavorable” and investigate how the RCON model copes with these problems. Comparing problem-solving performances of the oscillation modes, we show that there exist some modes often attain socially favorable patterns without being trapped in the greedy one.     

Amoeba-based Chaotic Neurocomputing: Combinatorial Optimization by Coupled Biological Oscillators

We demonstrate a neurocomputing system incorporating an amoeboid unicellular organism, the true slime mold Physarum, known to exhibit rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. Introducing optical feedback applied according to a recurrent neural network model, we induce that the amoeba’s photosensitive branches grow or degenerate in a network-patterned chamber in search of an optimal solution to the traveling salesman problem (TSP), where the solution corresponds to the amoeba’s stably relaxed configuration (shape), in which its body area is maximized while the risk of being illuminated is minimized.Our system is capable of reaching the optimal solution of the four-city TSP with a high probability. Moreover, our system can find more than one solution, because the amoeba can coordinate its branches’ oscillatory movements to perform transitional behavior among multiple stable configurations by spontaneously switching between the stabilizing and destabilizing modes. We show that the optimization capability is attributable to the amoeba’s fluctuating oscillatory movements. Applying several surrogate data analyses, we present results suggesting that the amoeba can be characterized as a set of coupled chaotic oscillators.

Networked reinforcement learning

Recently, many models of reinforcement learning with hierarchical or modular structures have been proposed. They decompose a task into simpler subtasks and solve them by using multiple agents. However, these models impose certain restrictions on the topological relations of agents and so on. By relaxing these restrictions, we propose networked reinforcement learning, where each agent in a network acts autonomously by regarding the other agents as a part of its environment. Although convergence to an optimal policy is no longer assured, by means of numerical simulations, we show that our model functions appropriately, at least in certain simple situations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Bayesian inference explains perception of unity and ventriloquism aftereffect: identification of common sources of audiovisual stimuli

We study a computational model of audiovisual integration by setting a Bayesian observer that localizes visual and auditory stimuli without presuming the binding of audiovisual information. The observer adopts the maximum a posteriori approach to estimate the physically delivered position or timing of presented stimuli, simultaneously judging whether they are from the same source or not. Several experimental results on the perception of spatial unity and the ventriloquism effect can be explained comprehensively if the subjects in the experiments are regarded as Bayesian observers who try to accurately locate the stimulus. Moreover, by adaptively changing the inner representation of the Bayesian observer in terms of experience, we show that our model reproduces perceived spatial frame shifts due to the audiovisual adaptation known as the ventriloquism aftereffect.