Alkylidinphosphane und -arsane. I [P ≡ C?S]−[Li(dme)3]+ – Synthese und Struktur

Alkylidynephosphanes and -arsanes. I [P ≡ C? S]^?[Li(dme)₃]⁺ – Synthesis and Structure O,O′-Diethyl thiocarbonate and bis(tetrahydrofuran)-lithium bis(trimethylsilyl)phosphanide dissolved in 1,2-dimethoxyethane, react below 0°C to give ethoxy trimethylsilane and tris(1,2-dimethoxyethane-O,O′)lithium 2λ³-phosphaethynylsulfanide – [P≡C? S]^? [Li(dme)₃]⁺ – ( 1a ). Apart from bis(trimethylsilyl)sulfane or carbon oxide sulfide, dark red concentrated solutions of λ³-phosphaalkyne 1 are also obtained from reactions of carbon disulfide with bis(tetrahydrofuran)-lithium bis(trimethylsilyl)phosphanide or with the homologous lithoxy-methylidynephosphane ( 2 ) [1]. The ir spectrum shows two absorptions at 1762 and 747 cm^?1 characteristic for the P≡C and C? S stretching vibrations. The nmr parameters {δ(³¹P) ? 121.3; δ(¹³C) 190.8 ppm; ¹J_CP 18.2 Hz} resemble much more values of diorganylamino-2λ³-phosphaalkynes than those of bis(1,2-dimethoxyethane-O,O′)lithoxy-methylidyne-phosphane ( 2a ). As found by an X-ray structure analysis (P2₁/c; a = 1192.6(16); b = 1239.1(19); c = 1414.8(26) pm; β = 105.91(13)° at ?100 ± 3°C; Z = 4 formula units; wR = 0.064) of pale yellow crystals (mp. + 16°C) isolated from the reaction with O,O′-diethyl thiocarbonate, the solid is built up of separate [P≡C? S]^? and [Li(dme)₃]⁺ ions. Typical bond lengths and angles are: P≡C 155.5(11); C? S 162.0(11); Li? O 206.4(17) to 220.3(20) pm; P≡C? S 178.9(7)°.     

Ein Neues,tricyclisches schwefelreiches Phosphan

A New Tricyclic Sulfur-rich Phosphane From the products of the reaction of P₄S₃ with tert-Butyliodide the compound (t-Bu(S)P)₂P₄S₃ was isolated in low yield. The compound is stable and has a structure related to the hydrocarbon brexane. By reaction with triphenylphosphin the compounds (t-BuP)(t-Bu(S)P)P₄S₃ and (t-BuP)₂P₄S₃ were obtained. The ³¹P-nmr spectra of all compounds were solved and used to determine the structure of the molecules. A complete set of ³¹P-nmr data is given.     

Investigation of the Curing Mechanism in the GPTS/APTS Binder System

The acid catalyzed sol-gel reaction in the mixed binder system, 3-glycidoxypropyltrimethoxysilane (GPTS)/3-aminopropyltriethoxysilane (APTS) was investigated and one step and two step synthesis process were compared. Hydrolysis product was observed using the ¹H, ¹³C NMR and Raman spectra. Especially, based on the Raman spectra, epoxy ring opening was observed, varying the ratio of GPTS to APTS. The two step process made clear sol, while the one step process resulted in a milky suspension. According to the Raman spectra, the epoxy ring opening reaction kinetics proceeded slower in the two step process than one step process. Throughout the two step process, it was possible to apply the binder for the coating of substrate.     

TM模场入射到金属光栅的表面增强喇曼散射计算分析

以纳米量级金属履带矩形光栅为模型,研究了表面增强喇曼散射的特性.针对TM模的入射光,采用严格的耦合波原理对金属表面的衍射场进行了分析,并用数值计算方法讨论了光栅深度、光栅占空比等参量对金属表面增强的影响以及增强因子随光栅深度的变化关系.结果显示：当入射光波长为700 nm、入射角度为10°、光栅周期p=600 nm、 光栅深度d=37.5 nm、占空比为1/3时,获得最大增强,增强因子G达104倍.     

cBN晶体的Raman光谱测量

 用R1000激光共聚焦Raman光谱仪研究了高温高压合成棒中的立方氮化硼（cBN）晶体、原材料六方氮化硼（hBN）和催化剂。Raman光谱测量结果表明：伴随cBN晶体生长的散射峰,出现了两条全新的Raman散射峰（约1.088 cm^-1和约1.368 cm^-1）。该散射峰所对应的物质可能是在高温高压条件下hBN向cBN转变时生成的不完全产物——BN的一种新相。这一结果将有助于进一步讨论cBN的生长机理。     

A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry

We propose an innovative delta-differencing algorithm that combines software-updating methods with LZ77 data compression. This software-updating method relates to server-side software that creates binary delta files and to client-side software that performs software-update installations. The proposed algorithm creates binary-differencing streams already compressed from an initial phase. We present a software-updating method suitable for OTA software updates and the method’s basic strategies to achieve a better performance in terms of speed, compression ratio or a combination of both. A comparison with publicly available solutions is provided. Our test results show our method, Keops, can outperform an LZMA (Lempel–Ziv–Markov chain-algorithm) based binary differencing solution in terms of compression ratio in two cases by more than 3% while being two to five times faster in decompression. We also prove experimentally that the difference between Keops and other competing delta-creator software increases when larger history buffers are used. In one case, we achieve a three times better performance for a delta rate compared to other competing delta rates.     

Low-Complexity Alternatives to the Optimal Linear Coding Scheme for Transmitting ARMA Sources

In the era of the Internet of Things, there are many applications where numerous devices are deployed to acquire information and send it to analyse the data and make informed decisions. In these applications, the power consumption and price of the devices are often an issue. In this work, analog coding schemes are considered, so that an ADC is not needed, allowing the size and power consumption of the devices to be reduced. In addition, linear and DFT-based transmission schemes are proposed, so that the complexity of the operations involved is lowered, thus reducing the requirements in terms of processing capacity and the price of the hardware. The proposed schemes are proved to be asymptotically optimal among the linear ones for WSS, MA, AR and ARMA sources.     

钝头机体用FADS-α的求解算法及精度研究

针对钝头机体用嵌入式大气数据传感(flush air data sensing,FADS)系统的4类攻角求解算法及算法的求解精度进行研究.针对典型的15°钝头体外形,在Mach数Ma=2.04,3.02,5.01,攻角α=-5°～30°,侧滑角β=0°的条件下,首先基于势流理论及修正的Newton流理论建立了钝头机体用...     

Neural Networks for Financial Time Series Forecasting

Financial and economic time series forecasting has never been an easy task due to its sensibility to political, economic and social factors. For this reason, people who invest in financial markets and currency exchange are usually looking for robust models that can ensure them to maximize their profile and minimize their losses as much as possible. Fortunately, recently, various studies have speculated that a special type of Artificial Neural Networks (ANNs) called Recurrent Neural Networks (RNNs) could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The results show that the GRU gives the overall best results, especially for the univariate out-of-sample forecasting for the currency exchange rates and multivariate out-of-sample forecasting for the stock market indexes.     

金融发展对经济增长影响的实证分析

本文将金融发展作为一个独立解释变量引入,构造经济增长的面板数据模型,运用面板数据以1994年为分界点分两阶段实证分析了全国及东、中、西部地区1985~2003年金融发展对经济增长的影响,以及东、中、西部地区影响的差异性,模型较好地拟合了数据。实证分析表明,各地区之间金融发展的不平衡性可以部分解释其经济增长的差异性。