Reaction of 3,3-dichloropentane-2,4-dione with aromatic aldehydes under the conditions of Darzens reaction

3,3-Dichloropentane-2,4-dione reacts with aromatic aldehydes under the conditions of Darzens reaction to give 4-acetoxy-4-aryl-3,3-dichlorobutan-2-ones, the products of insertion into the -C-C bond. The reaction of ethyl dichloroacetylacetate with benzaldehyde yields a derivative of tricyclo[5.1.0.0^3,5]octane, rather than 2,6-bis(1-chlorobenzylidene)cyclohexane-1,4-dione, as the by-product.Translated fromIzvestiya Akademii Nauk. Seriya Khimicheskaya, No. 7, pp. 1294–1298, July, 1995.     

Carbocyclic and nitrogen-containing fused CH-acids with an annelated indenyl fragment. Thermogravimetric and X-ray structural analysis of 6H-indeno[1,2-b]quinoline

Based on thermogravimetric characteristics first obtained for the model 6H-indeno [1,2-b]quinoline, the scheme of thermal conversions of this compound in the temperature range 20–700 °C has been proposed, and the limit of its thermal stability (300 °C) has been determined. This temperature is recommended as the optimum for synthesizing fused benzoaza(diaza)fluorenes. Based on the results of X-ray structural analysis, the molecules of the studied indenoquinoline form centrosymmetric pairs, which are arranged in (110) layers. The molecules are orientationally disordered. The observed self-association of these molecules is similar to the - association of fused heterocyclic systems with-excessive and ****- deficient fragments. It has been suggested that interferon-inducing and antitumor compounds with an annelated indenyl fragment have a common mechanism of action according to the intercalation model of stacking structures.Translated fromIzvestiya Akademii Nauk. Seriya Khimicheskaya, No. 4, pp. 703–709, April, 1995.     

Lines of curvature near umbilical points on surfaces immersed in R4

LetM be a compact orientable surface,I:M R⁴ an immersion of classC ^r and a normal unitary smooth vector field. We prove that in the space of pairs (I, ) the family of pairs all whose umbilical points are Darbouxian is generic.     

Real-time onboard wind and windshear determination,part 2: Detection

This paper is concerned with windshear detection in connection with real-time wind identification (Ref. 1). It presents a comparative evaluation of two techniques, one based on the shear/downdraft factor and one based on the wind difference index. The comparison is done with reference to a particular microburst, that which caused the 1985 crash of Flight Delta 191 at Dallas-Fort Worth International Airport.The shear/downdraft factor has the merit of combining the effects of the shear and the downdraft into a single entity. However, its effectiveness is hampered by the fact that, in a real situation, the windshear is accompanied by free-stream turbulence, which tends to blur the resulting signal. In turn, this results in undesirable nuisance warnings if the magnitude of the shear factor due to free-stream turbulence is temporarily larger than that due to true windshear. Therefore, proper filtering is necessary prior to using the shear/downdraft factor in detection and guidance. One effective way for achieving this goal is to average the shear/downdraft factor over a specified time interval . The effect of on the average shear/downdraft factor is studied.     

Real-time onboard wind and windshear determination,part 1: Identification

Standard wind identification techniques employed in the analysis of aircraft accidents are post-facto techniques; they are processed after the event has taken place and are based on the complete time histories of the DFDR/ATCR data along the entire trajectory. By contrast, real-time wind identification techniques are processed while the event is taking place; they are based solely on the knowledge of the preceding time histories of the DFDR/ATCR data.In this paper, a real-time wind identification technique is developed. First, a 3D-kinematic approach is employed in connection with the DFDR/ATCR data covering the time interval preceding the present time instant. The aircraft position, inertial velocity, and accelerometer bias are determined by matching the flight trajectory computed from the DFDR data with the flight trajectory available from the ATCR data. This leads to a least-square problem, which is solved analytically every seconds, with / small.With the inertial velocity and accelerometer bias known, an extrapolation process takes place so as to predict the inertial velocity profile over the subsequent -subinterval. At the end of this subinterval, the extrapolated inertial velocity and the newly identified inertial velocity are statistically reconciled and smoothed. Then, the process of identification, extrapolation, reconciliation, and smoothing is repeated. Subsequently, the wind is computed as the difference between the inertial velocity and the airspeed, which is available from the DFDR data. With the wind identified, windshear detection can take place (Ref. 1).As an example, the real-time wind identification technique is applied to Flight Delta 191, which crashed at Dallas-Fort Worth International Airport on August 2, 1985. The numerical results show that the wind obtained via real-time identification is qualitatively and quantitatively close to the wind obtained via standard identification. This being the case, it is felt that real-time wind identification can be useful in windhsear detection and guidance, above all if the shear/downdraft factor signal is replaced by the wind difference signal (Ref. 1).This paper and its companion (Ref. 1) are based on Refs. 2–4.This research was supported by the Aviation Research and Education Foundation and by Texas Advanced Technology Program, Grant No. TATP-003604020.     

Fastiterative methods for least squares estimations

Least squares estimations have been used extensively in many applications, e.g. system identification and signal prediction. When the stochastic process is stationary, the least squares estimators can be found by solving a Toeplitz or near-Toeplitz matrix system depending on the knowledge of the data statistics. In this paper, we employ the preconditioned conjugate gradient method with circulant preconditioners to solve such systems. Our proposed circulant preconditioners are derived from the spectral property of the given stationary process. In the case where the spectral density functions() of the process is known, we prove that ifs() is a positive continuous function, then the spectrum of the preconditioned system will be clustered around 1 and the method converges superlinearly. However, if the statistics of the process is unknown, then we prove that with probability 1, the spectrum of the preconditioned system is still clustered around 1 provided that large data samples are taken. For finite impulse response (FIR) system identification problems, our numerical results show that annth order least squares estimator can usually be obtained inO(n logn) operations whenO(n) data samples are used. Finally, we remark that our algorithm can be modified to suit the applications of recursive least squares computations with the proper use of sliding window method arising in signal processing applications.Research supported in part by HKRGC grant no. 221600070, ONR contract no. N00014-90-J-1695 and DOE grant no. DE-FG03-87ER25037.     

光伏模组积灰与阴影特性分析及识别方法

针对光伏模组积灰与阴影特性识别问题,详细分析了积灰和阴影的光伏特性曲线差异,揭示了阴影光伏曲线的拐点时变特性。提出由特性曲线的拐点数量及电流电压特性条件共同构成训练模型的输入特征量,基于CatBoost算法训练积灰和阴影识别模型。最后,利用光伏模组实测数据对CatBoost算法、ID3和GA-BP算法训练出的识别模型进行性能分析和对比测试,结果表明基于CatBoost训练出的识别模型输入量区分性强、诊断精度高,极具工程应用价值。     

Structural OH in mantle-derived clinopyroxene megacrysts from Nushan

Thirteen clinopyroxene megacrysts from Nushan Cenozoic basalts have been investigated by Fourier transform infrared spectrometer(FTIR). The results demonstrate that all samples contain trace amount of structural OH. Clinopyroxenes from Nushan show four groups of absorption bands, in which, 3 472–3 506 cm⁻¹ is the major characteristic one. It is suggested that the position of major band is probably related to AI content: the position of major characteristic band for high-AI samples is around 3 470 cm⁻¹, whereas that for low-AI samples around 3 620 cm⁻¹.