A Composite Formation Route to Well‐Crystalline Manganese Oxide Nanocrystals: High Catalytic Activity of Manganate–Alumina Nanocomposites

Manganese oxide nanocrystals are combined with aluminum oxide nanocrystals to improve their crystallinity via calcination without a significant increase of crystal size. A nanocomposite, consisting of two metal oxides, can be synthesized by the reaction between permanganate anions and aluminum oxyhydroxide keggin cations. The as‐prepared manganese oxide–aluminum oxide nanocomposite is X‐ray amorphous whereas heat‐treatment gives rise to the crystallization of an α‐MnO₂ phase at 600 °C and Mn₃O₄/Mn₂O₃ and γ‐Al₂O₃ phases at 800 °C. Electron microscopy and N₂ adsorption‐desorption‐isotherm analysis clearly demonstrate that the as‐prepared nanocomposite is composed of a porous assembly of monodisperse primary particles with a size of ～20 nm and a surface area of >410 m² g^?1. Of particular interest is that the small particle size of the as‐prepared nanocomposite is well‐maintained up to 600 °C, a result of the prevention of the growth of manganate grains through nanoscale mixing with alumina grains. The calcined nanocomposite shows very‐high catalytic activity for the oxidation of cyclohexene with an extremely high conversion efficiency of >95% within 15 min. The present results show that the improvement of the crystallinity without significant crystal growth is very crucial for optimizing the catalytic activity of manganese oxide nanocrystals.     

Human reliability in maintenance task

Maintenance management requirements have another dimension in the present as in the past. Looking for the optimal tools of decision processing is hard. The current concepts have represented the integration of theoretical, technical, and management tools whose applications need actual modification. Technical measurements are expensive and take a long time/term, and it is hard to estimate the economical returns. This paper presents the concept of a maintenance management task and its function in preventing major accidents. The importance of considering the reliability of maintenance tasks and the method to reach it are also discussed in this paper.     

Linearized microring-loaded Mach-Zehnder modulator with RF gain

A linearized modulator based on the Mach-Zehnder interferometer (MZI) loaded with a second-order allpass microring resonator is proposed and analyzed. The device is shown to exhibit much smaller intermodulation distortion than standard Mach-Zehnder modulators, thus greatly enhancing the device dynamic range. Gain effect associated with the microring resonance results in a modulator transfer function with high slope efficiency and large RF gain. Moreover, combined with the use of a highly electrooptic polymer, the high slope efficiency allows for the possibility of realizing modulators with subvolt drive voltage. The detrimental effect of loss in the microring resonators on device performance is also investigated and a design approach to mitigate its effect is presented. The proposed modulator can be linearized to any arbitrary nth order by loading the MZI with a higher order allpass microring resonator.     

Ergodic optimization of stochastic differential systems in wireless networks

Multidimensional Systems and Signal Processing - The present work is the second article in a couple of intertwined papers. They form complementary items on the same subject. They both address the...     

Synthesis of Imidazole‐Based AIEgens with Wide Color Tunability and Exploration of their Biological Applications

Research on aggregation‐induced emission (AIE) has become increasingly popular recently and various AIE luminogens (AIEgens) have been developed based on tetraphenylethene, hexaphenylsilole, distyrylanthracene, tetraphenylpyrazine, etc. However, facile tuning of the AIEgen emissions in a wide range remains challenging. Herein, a novel series of AIEgens is reported, based on imidazole‐cored molecular rotors, with facile synthesis and emission colors covering the whole visible spectrum. Moreover, these imidazole derivatives exhibit biological functions unique among the AIEgens, including mitochondria‐specific imaging and antifungal activity. Benefiting from the easy preparation and the tunable emission, the imidazole derivatives are expected to not only diversify the family of AIEgens but also enrich their biological applications.     

Reconstruction from Sparsely Sampled Data by ART with Interpolated Rays

After a brief discussion of the algebraic reconstruction techniques (ART), we introduce the attenuation problem in positron emission tomography (PET). We anticipate that a generalization of ART, the so-called cyclic subgradient projection (CSP) method, may be useful for solving this problem. This, however, has not been successfully realized, due to the fact that data collected by our proposed stationary PET detector ring are too sparsely sampled. That this is, in fact, a major problem is demonstrated by showing that ordinary ART produces reconstructions with unacceptably strong artifacts even on perfect (no attenuation) data collected according to the PET geometry. We demonstrate that the source of this artifact is the sparse sampling, and we propose the use of interpolated rays to overcome the problem. This approach is successful, as is illustrated by showing reconstructions from sparsely sampled data by ART with interpolated rays.     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.     

Electronically tunable floating inductance simulator

In this letter, a new floating inductance simulator circuit is presented. The proposed structure consists of only one grounded capacitor without any external resistors and two different active elements. The active elements are dual-output current-controlled current conveyor (DO-CCCII) and operational transconductance amplifier (OTA). The proposed inductance simulator can be tuned electronically by changing the biasing current of the DO-CCCIIs or by changing the current of the OTA. Moreover, the circuit does not require any conditions of component matching. It has a good sensitivity performance with respect to tracking errors. As an application, the proposed inductance simulator is used to construct a fourth-order resistively terminated LC band-pass filter. The theoretical analysis is verified by the SPICE simulation results.