基于电磁振动上料的茶梗和昆虫异物近红外光谱和荧光图像在线检测研究

茶叶是大众青睐的健康饮品之一,但茶叶在机器采收和加工过程中,容易混入茶梗和昆虫异物,污染茶叶、影响其质量安全,是未来应防范和检测的重点。X射线成像技术,根据食品基质和异物的密度差实施检测,广泛适用于金属异物并延伸至高密度塑料,但对于茶梗、昆虫这类低密度有机异物尚不适用,所以迫切需要研发新型无损检测技术和方法。针对片状茶叶重叠、遮掩异物的问题,提出了电磁振动上料辅助近红外光谱和荧光图像的检测方案,进行绿茶中的内源性异物茶梗和外源性异物昆虫的在线检测研究。通过电磁振动上料辅助近红外光谱和荧光成像系统,采集了600～1 050 nm范围的近红外光谱600条和RGB-N四通道图像各65幅。采用451条光谱进行建模,其余149条光谱作为预测集,评估模型的性能,比较了去趋势(Detrending)、多元散射校正(MSC)、标准正态变换(SNV)、变权重正态变换(VSN)、迭代自适应加权惩罚最小二乘法(airPLS)、不对称最小二乘法(ALS)、光程估计与校正(OPLEC)等不同光谱预处理方法的处理效果,其中OPLEC能较好地消除散射效应,偏最小二乘判别分析(PLS-DA)模型的正确识别率由78%提...     

Air transport in chute flows

The air bubble rise velocity in still water depends mainly on the bubble size and is basically influenced by buoyancy, viscosity and surface tension. In high-speed flows the number of forces acting on air bubbles increases with turbulence, non-hydrostatic pressure gradient, shear forces, bubble clouds and free-surface entrainment. Air bubbles in these flows are used for cavitation protection of hydraulic structures such as chutes, spillways and bottom outlets. Here, air is normally added by means of aerators upstream of regions where the cavitation number falls below a critical value mainly to reduce the sonic velocity of the fluid and cushion the cavitation bubble collapse process. The distance between successive aerators depends basically on the bubble rise velocity. Until today, the bubble rise velocity in high-speed flows was not thoroughly investigated because of limited laboratory instrumentation. The present project focused on the streamwise development of air concentrations in high-speed flows along a 14 m long model chute. The bubble rise velocity was indirectly derived from the air detrainment gradient of the air concentration contour lines downstream of an aeration device. It accounts for the main hydraulic parameters chute slope, Froude number and air concentration. It is demonstrated that the bubble rise velocity in high-speed flow and stagnant water differ significantly due to fracturing processes, turbulence, and the ambient air concentration.     

Postbuckling prediction of double-walled carbon nanotubes under axial compression

An elastic double-shell model is presented for the buckling and postbuckling of a double-walled carbon nanotube subjected to axial compression. The analysis is based on a continuum mechanics model in which each tube of a double-walled carbon nanotube is described as an individual elastic shell and the interlayer friction is negligible between the inner and outer tubes. The governing equations are based on the Karman–Donnell-type nonlinear differential equations. The van der Waals interaction between the inner and outer nanotubes and the nonlinear prebuckling deformations of the shell are both taken into account. A boundary layer theory of shell buckling is extended to the case of double-walled carbon nanotubes under axial compression. A singular perturbation technique is employed to determine the buckling loads and postbuckling equilibrium paths. Numerical results reveal that the single-walled carbon nanotube and the double-walled carbon nanotube both have an unstable postbuckling behavior.     

Cp2ZrCl2-catalyzed synthesis of 2-aminovinyl benzimidazoles under microwave conditions

A microwave-assisted general method for the synthesis of 2-aminovinyl benzimidazoles has been developed.Treatment of the 1,2-phenylenediamines and N-arylated/N,N-dialkylated 3-aminoacroleins with bis(cyclopentadienyl)zirconium(IV) dichloride(Cp2Zr Cl2) as the catalyst under microwave irradiation for 3–5 min followed by in situ Mn O2 oxidation afforded thirteen 2-aminovinyl benzimidazoles in good yields.     

Electrochemical recognition of tryptophan enantiomers using self-assembled diphenylalanine structures induced by graphene quantum dots,chitosan and CTAB

Molecular self-assembly offers a promising route to the preparation of advanced materials for the construction of novel chiral sensing devices, and the inspiration for the development of such systems is often derived from simple biological models. Diphenylalanine (FF), an extensively studied short peptide, can self-assemble into highly ordered nano-/micro-structures. Here we report the electrochemical recognition of tryptophan enantiomers using three FF self-assembled structures produced in the presence of graphene quantum dots (GQDs), chitosan (CS) and cetyltrimethylammonium bromide (CTAB). Although the difference in the peak potentials of the enantiomers is very small, enantiomeric differences can be detected by the magnitude of the DPV current signals. The recognition efficiencies of the three self-assembled materials are different, due to the different structures formed during the self-assembly process.     

Haezendonck–Goovaerts risk measure with a heavy tailed loss

Recently Haezendonck–Goovaerts (H–G) risk measure has received much attention in (re)insurance and portfolio management. Some nonparametric inferences have been proposed in the literature. When the loss variable does not have enough moments, which depends on the involved Young function, the nonparametric estimator in Ahn and Shyamalkumar (2014) has a nonnormal limit, which challenges interval estimation. Motivated by the fact that many loss variables in insurance and finance could have a heavier tail such as an infinite variance, this paper proposes a new estimator which estimates the tail by extreme value theory and the middle part nonparametrically. It turns out that the proposed new estimator always has a normal limit regardless of the tail heaviness of the loss variable. Hence an interval with asymptotically correct confidence level can be obtained easily either by the normal approximation method via estimating the asymptotic variance or by a bootstrap method. A simulation study and real data analysis confirm the effectiveness of the proposed new inference procedure for estimating the H–G risk measure.