The matrix converter drive performance under abnormal input voltage conditions

The matrix converter (MC) is a direct frequency conversion device with high input power quality and regeneration capability. As a device without energy storage elements, it has higher power density than pulse-width modulation (PWM) inverter drives. However, for the same reason, the AC line side disturbances can degrade its performance and reliability. In this paper, the behavior of the MC drive under abnormal input line voltage conditions has been investigated. A technique to eliminate the input current distortion due to the input voltage unbalance has been developed and its feasibility proven via computer simulations and laboratory experiments. The power line failure behavior has also been investigated and the rapid restarting capability of the MC drive has been demonstrated via laboratory experiments.     

The computational power of interactive recurrent neural networks

In classical computation, rational- and real-weighted recurrent neural networks were shown to be respectively equivalent to and strictly more powerful than the standard Turing machine model. Here, we study the computational power of recurrent neural networks in a more biologically oriented computational framework, capturing the aspects of sequential interactivity and persistence of memory. In this context, we prove that so-called interactive rational- and real-weighted neural networks show the same computational powers as interactive Turing machines and interactive Turing machines with advice, respectively. A mathematical characterization of each of these computational powers is also provided. It follows from these results that interactive real-weighted neural networks can perform uncountably many more translations of information than interactive Turing machines, making them capable of super-Turing capabilities.     

Effects of catalyst addition to coal on CO2 adsorption kinetics of coal and char

The surface area of Illinois No. 6 coal impregnated with a series of carbon gasification catalysts was measured by carbon dioxide adsorption before and after pyrolysis. Chars were prepared by pyrolysing the coal in flowing nitrogen at a low controlled rate. The adsorbate uptake was described by a logarithmic time dependence. The amounts of CO₂ adsorbed at a fixed pressure within 1 minute and at maximum coverage were used to describe the surface area associated with micropores larger than 0.5 nm in diameter and with the total surface area, respectively. Catalyst addition decreased the surface area of coal accessible to CO₂ in all cases. After pyrolysis, some of the chars showed a considerable increase, others a massive decrease, in their adsorption capacity.     

The Preparation of Phosphazene Crosslinked Cyclen Microspheres as Host for Cu<Superscript>2+</Superscript> Ions and Their Utilization as a Support Material for the Preparation of a Copper Nanocatalyst

In this study firstly, phosphazene crosslinked cyclen microspheres were synthesized. Then, supported copper nanoparticles were prepared on these phosphazene crosslinked cyclen microspheres for use as a metal catalyst. The prepared microparticles and microparticle-supported metal catalyst were characterized using FT-IR, SEM-EDX, TEM and XPS analysis. Also, the prepared metal composite was used as a catalyst for the reduction reaction of 4-nitrophenol in the presence of NaBH₄ in aqueous media. The catalytic activity of the Cu-cyclen composite catalyst was investigated using UV–Vis spectroscopy. The reduction studies were completed at four different temperatures (30–60?°C). The activation parameters were calculated from the obtained rate constants at the four different temperatures (30–60?°C). The activation energy, activation enthalpy and activation entropy for the reduction reaction of 4-NP in the presence of Cu-cyclen composite catalyst were calculated as 39.88, 36.56 and ?143.30 kJmol^?1, respectively. The total turnover frequency (TOF) for Cu-cyclen composite catalyst was 0.794 mol 4-NP (mol Cu)^?1 (min)^?1.     

The impact of the structural dimensions and optical properties of bulk material on the optical performance of diffraction microstructures: crystalline silicon gratings and applications

This paper examines the impact of the structural dimensions and optical properties of the bulk material on the optical performance of gratings and microstructures. We present a brief summary of a rigorous electromagnetic diffraction analysis, and the S-matrix propagation algorithm we have developed. Infrared spectral transmission measurements in the range 2.5–25 μm, along with a rigorous full-vector simulation, are presented. Good agreement between theory and experiment is obtained. The optical performances of various optical devices incorporating grating structures are presented. Such devices include narrow-band filters and polarizers, and three-dimensional photonics band-gap structures.     

On probabilistic analog automata

We consider probabilistic automata on a general state space and study their computational power. The model is based on the concept of language recognition by probabilistic automata due to Rabin (Inform. Control 3 (1963) 230) and models of analog computation in a noisy environment suggested by Maass and Orponen (Neural Comput. 10 (1998) 1071), and Maass and Sontag (Neural Comput. 11 (1999) 771). Our main result is a generalization of Rabin's reduction theorem that implies that under very mild conditions, the computational power of such automata is limited to regular languages.     

Displacement of preference as a function of avoidance-avoidance conflict.

"An earlier study [see 34: 804]… has demonstrated that approach and avoidance gradients of preference could be obtained from children, following positive or negative reinforcement (success or failure) applied to their response to a training task." This experiment is an extension of that study, and attempts to establish a condition under which preference displacement may occur. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

FAK-Mediated Signaling Controls Amyloid Beta Overload,Learning and Memory Deficits in a Mouse Model of Alzheimer’s Disease

The non-receptor focal adhesion kinase (FAK) is highly expressed in the central nervous system during development, where it regulates neurite outgrowth and axon guidance, but its role in the adult healthy and diseased brain, specifically in Alzheimer’s disease (AD), is largely unknown. Using the 3xTg-AD mouse model, which carries three mutations associated with familial Alzheimer’s disease (APP KM670/671NL Swedish, PSEN1 M146V, MAPT P301L) and develops age-related progressive neuropathology including amyloid plaques and Tau tangles, we describe here, for the first time, the in vivo role of FAK in AD pathology. Our data demonstrate that while site-specific knockdown in the hippocampi of 3xTg-AD mice has no effect on learning and memory, hippocampal overexpression of the protein leads to a significant decrease in learning and memory capabilities, which is accompanied by a significant increase in amyloid β (Aβ) load. Furthermore, neuronal morphology is altered following hippocampal overexpression of FAK in these mice. High-throughput proteomics analysis of total and phosphorylated proteins in the hippocampi of FAK overexpressing mice indicates that FAK controls AD-like phenotypes by inhibiting cytoskeletal remodeling in neurons which results in morphological changes, by increasing Tau hyperphosphorylation, and by blocking astrocyte differentiation. FAK activates cell cycle re-entry and consequent cell death while downregulating insulin signaling, thereby increasing insulin resistance and leading to oxidative stress. Our data provide an overview of the signaling networks by which FAK regulates AD pathology and identify FAK as a novel therapeutic target for treating AD.