Frequency stabilization for mobile satellite terminals via LORAN

Digital satellite communication systems require careful management of frequency stability. Historically, frequency stability has been accomplished by continuously powered, high cost, high performance reference oscillators. Today's low cost mobile satellite communication equipment must operate under wide ranging environmental conditions, stabilize quickly after application of power, and provide adequate performance margin to overcome RF link impairments unique to the land mobile environment. Methods for frequency stabilization in land mobile applications must meet these objectives without incurring excessive performance degradation. A frequency stabilization scheme utilizing the LORAN (Long Range Navigation) system is presented

Modeling Elementary Heterogeneous Chemistry and Electrochemistry in Solid-Oxide Fuel Cells

This paper presents a new computational framework for modeling chemically reacting flow in anode-supported solid-oxide fuel cells (SOFC). Depending on materials and operating conditions, SOFC anodes afford a possibility for internal reforming or catalytic partial oxidation of hydrocarbon fuels. An important new element of the model is the capability to represent elementary heterogeneous chemical kinetics in the form of multistep reaction mechanisms. Porous-media transport in the electrodes is represented with a dusty-gas model. Charge-transfer chemistry is represented in a modified Butler-Volmer setting that is derived from elementary reactions, but assuming a single rate-limiting step. The model is discussed in terms of systems with defined flow channels and planar membrane-electrode assemblies. However, the underlying theory is independent of the particular geometry. Examples are given to illustrate the model

Global Neurosurgery: The Unmet Need

Globally, the lack of access to basic surgical care causes 3 times as much deaths as HIV/AIDS, tuberculosis, and malaria combined. The magnitude of this unmet need has been described recently, and the numbers are startling. Major shifts in global health agenda have highlighted access to essential and emergency surgery as a high priority. A broad examination of the current global neurosurgical efforts to improve access has revealed some strengths, particularly in the realm of training; however, the demand grossly outstrips the supply; most people in low-income countries do not have access to basic surgical care, either due to lack of availability or affordability. Projects that help create a robust and resilient health system within low- and middle-income countries require urgent implementation. In this context, concurrent scale-up of human resources, investments in capacity building, local data collection, and analysis for accurate assessment are essential. In addition, through process of collaboration and consensus building within the neurosurgical community, a unified voice of neurosurgery is necessary to effectively advocate for all those who need neurosurgical care wherever, whenever

On Generalisation of Dual-Thermocouple Sensor Characterisation to RTDs

Intrusive temperature sensors such as thermocouples and resistance temperature detectors (RTDs) have become industry standards for simple and cost-effective temperature measurement. However, many situations require the use of physically robust and therefore low bandwidth temperature sensors. Much work has been published on dual-thermocouple thermometry as a means of obtaining increased sensor bandwidth from relatively robust thermocouples, which are assumed to have firstorder response. This contribution seeks to determine if RTDs, which are known to have approximately first-order response [1], can also be characterised using the dual-thermocouple approach. Experimental results show that the response of an RTD cannot be represented by a first-order model with sufficient accuracy to allow successful application of this method. Furthermore, simulation studies demonstrated that if a sensor exhibits even marginally second-order response, highly inaccurate temperature reconstructions follow. It is concluded that a higher-order model that more accurately reflects RTD response would be required for successful dual-RTD characterisation

Modeling Electrochemical Oxidation of Hydrogen on Ni–YSZ Pattern Anodes

A computational model is developed to represent the coupled behavior of elementary chemistry, electrochemistry, and transport in the vicinity of solid-oxide fuel cell three-phase boundaries. The model is applied to assist the development and evaluation of H_2 charge-transfer reaction mechanisms for Ni–yttria-stabilized zirconia (YSZ) anodes. Elementary chemistry and surface transport for the Ni and YSZ surfaces are derived from prior literature. Previously published patterned-anode experiments [J. Mizusaki et al., Solid State Ionics, 70/71, 52 (1994)] are used to evaluate alternative electrochemical charge-transfer mechanisms. The results show that a hydrogen-spillover mechanism can explain the Mizusaki polarization measurements over wide ranges of gas-phase composition with both anodic and cathodic biases

Deep Gate Recurrent Neural Network

Abstract This paper explores the possibility of using multiplicative gate to build two recurrent neural network structures. These two structures are called Deep Simple Gated Unit (DSGU) and Simple Gated Unit (SGU), which are structures for learning long-term dependencies. Compared to traditional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM, which require more than one gate to control information flow in the network, SGU and DSGU only use one multiplicative gate to control the flow of information. We show that this difference can accelerate the learning speed in tasks that require long dependency information. We also show that DSGU is more numerically stable than SGU. In addition, we also propose a standard way of representing the inner structure of RNN called RNN Conventional Graph (RCG), which helps to analyze the relationship between input units and hidden units of RNN

EXPRSS: an Illumina based high-throughput expression-profiling method to reveal transcriptional dynamics

Background: Next Generation Sequencing technologies have facilitated differential gene expression analysis through RNA-seq and Tag-seq methods. RNA-seq has biases associated with transcript lengths, lacks uniform coverage of regions in mRNA and requires 10–20 times more reads than a typical Tag-seq. Most existing Tag-seq methods either have biases or not high throughput due to use of restriction enzymes or enzymatic manipulation of 5’ ends of mRNA or use of RNA ligations.  Results: We have developed EXpression Profiling through Randomly Sheared cDNA tag Sequencing (EXPRSS) that employs acoustic waves to randomly shear cDNA and generate sequence tags at a relatively defined position (~150-200 bp) from the 3′ end of each mRNA. Implementation of the method was verified through comparative analysis of expression data generated from EXPRSS, NlaIII-DGE and Affymetrix microarray and through qPCR quantification of selected genes. EXPRSS is a strand specific and restriction enzyme independent tag sequencing method that does not require cDNA length-based data transformations. EXPRSS is highly reproducible, is high-throughput and it also reveals alternative polyadenylation and polyadenylated antisense transcripts. It is cost-effective using barcoded multiplexing, avoids the biases of existing SAGE and derivative methods and can reveal polyadenylation position from paired-end sequencing.  Conclusions: EXPRSS Tag-seq provides sensitive and reliable gene expression data and enables high-throughput expression profiling with relatively simple downstream analysis

Learnability of Non-I.I.D

Abstract Learnability has always been one of the most central problems in learning theory. Most previous studies on this issue were based on the assumption that the samples are drawn independently and identically according to an underlying (unknown) distribution. The i.i.d. assumption, however, does not hold in many real applications. In this paper, we study the learnability of problems where the samples are drawn from empirical process of stationary β-mixing sequence, which has been a widely-used assumption implying a dependence weaken over time in training samples. By utilizing the independent blocks technique, we provide a sufficient and necessary condition for learnability, that is, average stability is equivalent to learnability with AERM (Asymptotic Empirical Risk Minimization) in the non-i.i.d. learning setting. In addition, we also discuss the generalization error when the test variable is dependent on the training sample