Insight into the synergistic mechanism of sonolysis and sono-induced BiFeO3 nanorods piezocatalysis in atenolol degradation: Ultrasonic parameters, ROS and degradation pathways.

Herein, BiFeO3 nanorods (BFO NRs) was synthesized as the piezoelectric catalyst. The synergistic mechanism of sonolysis and sono-induced BFO-piezocatalysis in atenolol degradation was revealed and the effect of ultrasonic parameters on it was investigated for the first time. The results indicated that 100 kHz was the optimal frequency for the sonolytic and sono-piezocatalytic degradation of atenolol in ultrasound/BFO nanorods (US/BFO NRs) system, with the highest synergistic coefficient of 3.43. The piezoelectric potential differences of BFO NRs by COMSOL Multiphysics simulations further distinguishing that the impact of cavitation shock wave and ultrasonic vibration from sonochemistry reaction (i.e., 2.48, -2.48 and 6.60 V versus 0.008, -0.008 and 0.02 V under tensile, compressive and shear stress at 100 kHz). The latter piezoelectric potentials were insufficient for reactive-oxygen-species (ROS) generation, while the former contributed to 53.93% •OH yield in US/BFO NRs system. Sono-piezocatalysis was found more sensitive to ultrasonic power density than sonolysis. The quenching experiments and ESR tests indicated that the ROS contribution in atenolol degradation followed the order of •OH > 1O2 > h+ > O2•- in US/BFO NRs system and 1O2 generation is exclusively dissolved-oxygen dependent. Four degradation pathways for atenolol in US/BFO NRs system were proposed via products identification and DFT calculation. Toxicity assessment by ECOSAR suggested the toxicity of the degradation products could be controlled.

Enhanced bezafibrate degradation and power generation via the simultaneous PMS activation in visible light photocatalytic fuel cell.

A collaborative system including peroxymonosulfate (PMS) activation in a photocatalytic fuel cell (PFC) with an BiOI/TiO2 nanotube arrays p-n type heterojunction as photoanode under visible light (PFC(BiOI/TNA)/PMS/vis system) was established. Xenon lamp was used as the light source of visible light. A 4.6 times higher pseudo-first-order bezafibrate (BZF) degradation rate constant was achieved in this system compared with the single PFC(BiOI/TNA)/vis system. The radical quenching experiments revealed that the contribution of reactive oxidative species (ROS) followed the order of 1O2 ≈ h+ >> •OH > SO4•- >>O2•-. The EPR tests demonstrated that PMS addition enlarged the formation of 1O2, •OH and SO4•-, but suppressed O2•- yield. Interestingly, 1O2 was further proved to dominantly originated from the priority reaction between positive photoinduced holes (h+) and negatively charged PMS. Besides, N2-purging tests and density functional theory calculation indicated that PMS probably reacted with residual photoinduced electron (e-) on the more negative conduction band (CB) of BiOI to form •OH and SO4•-, but competed with dissolved oxygen. Other e- transferred to the less negative CB of TNA through p-n junction will efficiently move to cathode through the external circuit. The greatly promoted power generation of PFC system was observed after PMS addition due to extra h+ consumption and efficient e- separation and transfer. Besides, three possible pathways for BZF degradation were proposed including hydroxylation, fibrate chain substituent and amino bond fracture. This study can provide new insights into the mechanisms of PMS assisted photocatalysis and accompanying energy recovery.

Weighted fusion of multiple models for wavelength selection.

A new method based on the weighted fusion of multiple models is presented for wavelength selection in multivariate calibration of spectral data. It fuses the regression coefficients of multiple models with weights based on minimum mean square error to improve the accuracy and stability of the wavelength selection. To validate the performance of the proposed method, it was applied to the partial least squares (PLS) modeling of three near-infrared spectral datasets and compared with full-spectrum PLS, genetic algorithm-based PLS, and uninformative variable elimination-based PLS methods. Results show that the proposed method can effectively select the informative wavelength and enhance the prediction ability of the PLS model. On account of its simpler algorithm and higher efficiency, it can be widely used in practical applications.

Compressed sensing MRI with singular value decomposition-based sparsity basis.

Compressed sensing MRI (CS-MRI) aims to significantly reduce the measurements required for image reconstruction in order to accelerate the overall imaging speed. The sparsity of the MR images in transformation bases is one of the fundamental criteria for CS-MRI performance. Sparser representations can require fewer samples necessary for a successful reconstruction or achieve better reconstruction quality with a given number of samples. Generally, there are two kinds of 'sparsifying' transforms: predefined transforms and data-adaptive transforms. The predefined transforms, such as the discrete cosine transform, discrete wavelet transform and identity transform have usually been used to provide sufficiently sparse representations for limited types of MR images, in view of their isolation to the object images. In this paper, we present singular value decomposition (SVD) as the data-adaptive 'sparsity' basis, which can sparsify a broader range of MR images and perform effective image reconstruction. The performance of this method was evaluated for MR images with varying content (for example, brain images, angiograms, etc), in terms of image quality, reconstruction time, sparsity and data fidelity. Comparison with other commonly used sparsifying transforms shows that the proposed method can significantly accelerate the reconstruction process and still achieve better image quality, providing a simple and effective alternative solution in the CS-MRI framework.

Compressed sensing MRI using Singular Value Decomposition based sparsity basis.

Magnetic Resonance Imaging (MRI) is an essential medical imaging tool limited by the data acquisition speed. Compressed Sensing is a newly proposed technique applied in MRI for fast imaging with the prior knowledge that the signals are sparse in a special mathematic basis (called the 'sparsity' basis). During the exploitation of the sparsity in MR images, there are two kinds of 'sparsifying' transforms: predefined transforms and data adaptive transforms. Conventionally, predefined transforms, such as the discrete cosine transform and discrete wavelet transform, have been adopted in compressed sensing MRI. Because of their independence from the object images, the conventional transforms can only provide ideal sparse representations for limited types of MR images. To overcome this limitation, this work proposed Singular Value Decomposition as a data-adaptive sparsity basis for compressed sensing MRI that can potentially sparsify a broader range of MRI images. The proposed method was evaluated by a comparison with other commonly used predefined sparsifying transformations. The comparison shows that the proposed method could give a sparser representation for a broader range of MR images and could improve the image quality, thus providing a simple and effective alternative solution for the application of compressed sensing in MRI.

Comparison and analysis of nonlinear algorithms for compressed sensing in MRI.

Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A systematic and comparative study of those commonly used algorithms is therefore essential for the implementation of CS in MRI. In this work, three typical algorithms, namely, the Gradient Projection For Sparse Reconstruction (GPSR) algorithm, Interior-point algorithm (l(1)_ls), and the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm are compared and investigated in three different imaging scenarios, brain, angiogram and phantom imaging. The algorithms' performances are characterized in terms of image quality and reconstruction speed. The theoretical results show that the performance of the CS algorithms is case sensitive; overall, the StOMP algorithm offers the best solution in imaging quality, while the GPSR algorithm is the most efficient one among the three methods. In the next step, the algorithm performances and characteristics will be experimentally explored. It is hoped that this research will further support the applications of CS in MRI.

[A new wavelength selection algorithm based on the fusion of multiple models].

NIR spectroscopy makes a feature of a large number of wavelengths with a much smaller set of samples. However, some of the wavelengths contribute no information to the modeling. Even worse, they may contain the irrelevant information such as noise and background, which may result in a complex model and/or bad predictive ability of the model. So, it's important to do research in-depth to eliminate these wavelengths and improve the quality of the final model. The present paper firstly summarizes the variable selection methods based on a single PLS regression model and concludes that (1) the cross-validation can be used to select optimal model with good predictive ability, but the resulting model may be not suitable for selecting variables; (2) selecting variables based on a single regression model is inaccurate and instable because a single vector of regression coefficients may not measure the importance of the variables correctly and may vary with models of different complexity. On basis of this analysis, this paper proposed a new method for variable selection based on the fusion of multiple PLS models. This method fuses the multiple PLS regression coefficients to form a vector, then a threshold is determined to eliminate the variables whose corresponding element in the vector is lower than this threshold. Finally, this method is verified by 3 well-known NIR datasets and compared with the UVE-PLS and GA-PLS algorithms. The experiments show that this method may result in a model with less complexity and/or better predictive ability. Moreover, the proposed method is elegant and efficient and therefore can be put in practical use.

[Research on the near-infrared spectrometer system].

Near-infrared spectrometer is the integration of spectrum test technology, stoichiometry technology and computer technology. In the present paper, based on effective food ingredients and non-invasive quantitative detection, the development process of the micro-near-infrared spectrometer system was introduced. Spectrometer is the basis of the system. This paper focuses on the development of the micro-near-infrared spectrometer applicable to on-line real-time testing. A micro-near-infrared spectrometer prototype was developed successfully, its main technical parameter was tested, and the result shows: its operating wavelength is: 850-1 690 nm, optical resolution is: less than 10 nm, and its performance has achieved the level of the congener foreign products. Stoichiometric technology and computer technology is the core of the system. LS-LWR modeling methods were proposed. Finally, the quantitative test for glucose water solution using the micro-near-infrared spectrometer shows that the correlation coefficient of prediction model is 0.995, and the corresponding RMSEP is 0.06.

[A new algorithm for NIR modeling based on manifold learning].

Manifold learning is a new kind of algorithm originating from the field of machine learning to find the intrinsic dimensionality of numerous and complex data and to extract most important information from the raw data to develop a regression or classification model. The basic assumption of the manifold learning is that the high-dimensional data measured from the same object using some devices must reside on a manifold with much lower dimensions determined by a few properties of the object. While NIR spectra are characterized by their high dimensions and complicated band assignment, the authors may assume that the NIR spectra of the same kind of substances with different chemical concentrations should reside on a manifold with much lower dimensions determined by the concentrations, according to the above assumption. As one of the best known algorithms of manifold learning, locally linear embedding (LLE) further assumes that the underlying manifold is locally linear. So, every data point in the manifold should be a linear combination of its neighbors. Based on the above assumptions, the present paper proposes a new algorithm named least square locally weighted regression (LS-LWR), which is a kind of LWR with weights determined by the least squares instead of a predefined function. Then, the NIR spectra of glucose solutions with various concentrations are measured using a NIR spectrometer and LS-LWR is verified by predicting the concentrations of glucose solutions quantitatively. Compared with the existing algorithms such as principal component regression (PCR) and partial least squares regression (PLSR), the LS-LWR has better predictability measured by the standard error of prediction (SEP) and generates an elegant model with good stability and efficiency.