Generation of true quantum random numbers with on-demand probability distributions via single-photon quantum walks.

Random numbers are at the heart of diverse fields, ranging from simulations of stochastic processes to classical and quantum cryptography. The requirement for true randomness in these applications has motivated various proposals for generating random numbers based on the inherent randomness of quantum systems. The generation of true random numbers with arbitrarily defined probability distributions is highly desirable for applications, but it is very challenging.

The Rashba effect in two-dimensional hybrid perovskites: the impacts of halogens and surface ligands.

Two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) have gained much research interest nowadays due to their outstanding optoelectronic properties; however, the properties of the Rashba effect in 2D HOIPs have not been fully interpreted. In this work, a detailed thickness dependent structural distortion along with the Rashba splitting energy were investigated. Three types of HOIPs, 2D MAPbCl, 2D MAPbBr and 2D MAPbI, were adopted to compare the effect of halogens; and three surface ligands, BA, -BA and PEA, were adopted to explore the effect of ligands.

Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine.

Background: Behavior is an important indicator reflecting the welfare of animals. Manual analysis of video is the most commonly used method to study animal behavior. However, this approach is tedious and depends on a subjective judgment of the analysts.

[Progress in quality analysis of honey by infrared spectroscopy].

The detection of the quality of honey and the differentiation of adulteration are very important for quality and safety assurance. Traditionally used chemical methods were expensive and complicated, therefore they are not suitable for the requirement of wide-scale detection. In the past decade, the detection technology of honey developed with a trend of fast and high throughput detection.

[Possibilities of near-infrared spectroscopy for the assessment of principle components in honey].

The potential of near infrared spectroscopy (NIR) as a nondestructive method for determining the principle components of honeys was studied for 153 unifloral honeys and multifloral honey samples. Fourier transform near-infrared spectroscopy (FT-NIR), CCD near-infrared spectroscopy and PDA near-infrared spectroscopy were evaluated to quantitatively determine water content, fructose content and glucose content in honey. On the basis of partial-least square (PLS) regression, the models of honey were compared.

[Analysis of NIR characteristic wavelengths for apple flesh firmness based on GA and iPLS].

In the present study, the fruit flesh firmness of apple was analyzed by near infrared (NIR) spectroscopy using an FT-NIR spectrometer. The sensitive spectral regions that provide the lowest prediction error were analyzed by different well-known variable selection methods, including dynamic backward interval partial least-squares (dynamic biPLS), sequential application of backward interval partial least-squares and genetic algorithm(dynamic biPLS & GA-PLS), and iterative genetic algorithm partial least-squares (iterative GA-PLS). Iterative GA-PLS, dynamic biPLS & GA-PLS led to a distinct reduction in the number of spectral data points with better predictive quality.

The performance of nu-support vector regression on determination of soluble solids content of apple by acousto-optic tunable filter near-infrared spectroscopy.

The nu-support vector regression (nu-SVR) was used to construct the calibration model between soluble solids content (SSC) of apples and acousto-optic tunable filter near-infrared (AOTF-NIR) spectra. The performance of nu-SVR was compared with the partial least square regression (PLSR) and the back-propagation artificial neural networks (BP-ANN). The influence of SVR parameters on the predictive ability of model was investigated.

Study of wavelet denoising in apple's charge-coupled device near-infrared spectroscopy.

Discrete wavelet transform was used to eliminate the noise in the charge-coupled device near-infrared (CCD-NIR) spectra of apple. The influence of three parameters (wavelet function, decomposition level, and threshold) on the predictive ability of the calibration model was investigated. The result showed that the db, sym, and bior wavelet families performed well, while the coif, dmey, and haar wavelets were not able to denoise effectively.