Generation of uniform drops via through-hole arrays micromachined in stainless-steel plates

This study investigated the generation of oil drops using new symmetric and asymmetric through-hole-array devices made of stainless steel. The through-hole-array devices were built by piling up six stainless-steel plates, each having circular micro-holes with a diameter of 300 or 500 μm or micro-slots with a shorter line of 300 or 500 μm. Drops were generated by injecting a dispersed phase (refined soybean oil) via the through-hole array into a compartment filled with a continuous phase (Milli-Q water solution containing one of two emulsifiers). The drop detachment from symmetric and asymmetric through holes was observed in real time and analyzed. Uniform oil drops with average diameters of 1.0–4.1 mm and coefficients of variation of typically less than 6% were generated using symmetric and asymmetric through-hole-array devices. The resultant drop diameters for asymmetric through-hole arrays were significantly smaller than those for symmetric through-hole arrays. This paper also discusses experimental results regarding the effects of the microstructure, the dimensions of the through holes, and the type of emulsifier on drop generation and the resultant drop diameter.     

Straight-through microchannel devices for generating monodisperse emulsion droplets several microns in size

The authors recently proposed a promising technique for producing monodisperse emulsions using a straight-through microchannel (MC) device composed of an array of microfabricated oblong holes. This research developed new straight-through MC devices with tens of thousands of oblong channels of several microns in size on a silicon-on-insulator plate, and investigated the emulsification characteristics using the microfabricated straight-through MC devices. Monodisperse oil-in-water (O/W) and W/O emulsions with average droplet diameters of 4.4–9.8 μm and coefficients of variation of less than 6% were stably produced using surface-treated straight-through MC devices that included uniformly sized oblong channels with equivalent diameters of 1.7–5.4 μm. The droplet size of the resultant emulsions depended greatly on the size of the preceding oblong channels. The emulsification process using the straight-through MC devices developed in this research had very high apparent energy efficiencies of 47–60%, defined as (actual energy input applied to droplet generation/theoretical minimum energy input necessary for making droplets) × 100. Straight-through MC devices with numerous oblong microfluidic channels also have great potential for increasing the productivity of monodisperse fine emulsions.     

Dynamic diamond anvil cell (dDAC): a novel device for studying the dynamic-pressure properties of materials

We have developed a unique device, a dynamic diamond anvil cell (dDAC), which repetitively applies a time-dependent load/pressure profile to a sample. This capability allows studies of the kinetics of phase transitions and metastable phases at compression (strain) rates of up to 500 GPa/s (approximately 0.16 s(-1) for a metal). Our approach adapts electromechanical piezoelectric actuators to a conventional diamond anvil cell design, which enables precise specification and control of a time-dependent applied load/pressure. Existing DAC instrumentation and experimental techniques are easily adapted to the dDAC to measure the properties of a sample under the varying load/pressure conditions. This capability addresses the sparsely studied regime of dynamic phenomena between static research (diamond anvil cells and large volume presses) and dynamic shock-driven experiments (gas guns, explosive, and laser shock). We present an overview of a variety of experimental measurements that can be made with this device.     

Modeling expert problem solving in a game of chance: a Yahtzee© case study

Although developments on software agents have led to useful applications in automation of routine tasks such as electronic mail filtering, there is a scarcity of research that empirically evaluates the performance of a software agent versus that of a human reasoner, whose problem-solving capabilities the agent embodies. In the context of a game of a chance, namely Yahtzee©, we identified strategies deployed by expert human reasoners and developed a decision tree for agent development. This paper describes the computer implementation of the Yahtzee game as well as the software agent. It also presents a comparison of the performance of humans versus an automated agent. Results indicate that, in this context, the software agent embodies human expertise at a high level of fidelity.     

Variational Bayes solution of linear neural networks and its generalization performance

It is well known that in unidentifiable models, the Bayes estimation provides much better generalization performance than the maximum likelihood (ML) estimation. However, its accurate approximation by Markov chain Monte Carlo methods requires huge computational costs. As an alternative, a tractable approximation method, called the variational Bayes (VB) approach, has recently been proposed and has been attracting attention. Its advantage over the expectation maximization (EM) algorithm, often used for realizing the ML estimation, has been experimentally shown in many applications; nevertheless, it has not yet been theoretically shown. In this letter, through analysis of the simplest unidentifiable models, we theoretically show some properties of the VB approach. We first prove that in three-layer linear neural networks, the VB approach is asymptotically equivalent to a positive-part James-Stein type shrinkage estimation. Then we theoretically clarify its free energy, generalization error, and training error. Comparing them with those of the ML estimation and the Bayes estimation, we discuss the advantage of the VB approach. We also show that unlike in the Bayes estimation, the free energy and the generalization error are less simply related with each other and that in typical cases, the VB free energy well approximates the Bayes one, while the VB generalization error significantly differs from the Bayes one.     

Time dependent processing in a parallel pipeline architecture

Pipeline architectures provide a versatile and efficient mechanism for constructing visualizations, and they have been implemented in numerous libraries and applications over the past two decades. In addition to allowing developers and users to freely combine algorithms, visualization pipelines have proven to work well when streaming data and scale well on parallel distributed-memory computers. However, current pipeline visualization frameworks have a critical flaw: they are unable to manage time varying data. As data flows through the pipeline, each algorithm has access to only a single snapshot in time of the data. This prevents the implementation of algorithms that do any temporal processing such as particle tracing; plotting over time; or interpolation, fitting, or smoothing of time series data. As data acquisition technology improves, as simulation time-integration techniques become more complex, and as simulations save less frequently and regularly, the ability to analyze the time-behavior of data becomes more important. This paper describes a modification to the traditional pipeline architecture that allows it to accommodate temporal algorithms. Furthermore, the architecture allows temporal algorithms to be used in conjunction with algorithms expecting a single time snapshot, thus simplifying software design and allowing adoption into existing pipeline frameworks. Our architecture also continues to work well in parallel distributed-memory environments. We demonstrate our architecture by modifying the popular VTK framework and exposing the functionality to the ParaView application. We use this framework to apply time-dependent algorithms on large data with a parallel cluster computer and thereby exercise a functionality that previously did not exist.     

Learning and retention of associations between auditory icons and denotative referents: implications for the design of auditory warnings

OBJECTIVE: This study examined the way in which the type and preexisting strength of association between an auditory icon and a warning event affects the ease with which the icon/event pairing can be learned and retained. BACKGROUND: To be effective, an auditory warning must be audible, identifiable, interpretable, and heeded. Warnings consisting of familiar environmental sounds, or auditory icons, have potential to facilitate identification and interpretation. The ease with which pairings between auditory icons and warning events can be learned and retained is likely to depend on the type and strength of the preexisting icon/event association. METHOD: Sixty-three participants each learned eight auditory-icon/denotative-referent pairings and attempted to recall them 4 weeks later. Three icon/denotative-referent association types (direct, related, and unrelated) were employed. Participants rated the strength of the association for each pairing on a 7-point scale. RESULTS: The number of errors made while learning pairings was greater for unrelated than for either related or direct associations, whereas the number of errors made while attempting to recall pairings 4 weeks later was greater for unrelated than for related associations and for related than for direct associations. Irrespective of association type, both learning and retention performance remained at very high levels, provided the strength of the association was rated greater than 5. CONCLUSION: This suggests that strong preexisting associations are used to facilitate learning and retention of icon/denotative-referent pairings. APPLICATION: The practical implication of this study is that auditory icons having either direct or strong, indirect associations with warning events should be preferred.     

Experimental confirmation of the isotopic volume effect in superconducting molybdenum by means of energy-dispersive x-ray diffration at low temperatures

Differences in the lattice constants of Mo-100 and Mo-92 have been measured by x-ray diffraction, in order to search directly for the volume effect of isotopes in a superconductor. No significant difference in the lattice constanta(Mo-100)–a(Mo-92) could be detected at 290 K, while the differences –0.0014±0.0008 and –0.0029±0.0009 Å were detected at 85.3 and 4.31 K, respectively. These values, and their temperature dependence, are considered to be theoretically reasonable. The exponent in the isotope effect defined by T_cM^- is represented thermodynamically by =–( lnT _c / lnM)–( lnT _c / lnV)(d lnV/d lnM). From the results, d lnV/d lnM is found to be –0.033±0.009 at 4.31 K. Then, the second term representing the isotopic volume effect is estimated to be about 0.09, with lnT _c / lnV2.81. The observed value of is 0.33, so that the contribution of the second term, 0.09, is 27% of the value of . It becomes quite clear that the isotopic volume effect in superconducting Mo should not be neglected.     

基于平面模板的机器人TCF标定

针对末端夹持激光位移传感器的机器人TCF(tool/terminal control frame)标定,提出一种基于平面模板的标定方法,机器人操作末端执行器使激光位移传感器在不同位形下对平面模板进行测量,再通过非线性最小二乘拟合求解标定参数.为了减小问题的奇异性,对标定时应该采取的参数控制策略进行了定性分析.该标定方法只需要一块表面精度较高的平面模板,而无需其它测量仪器,标定过程简单、易操作,且易于实现自动化.仿真和实验结果表明本文提出的标定方法具有较高的精度.