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In this study, a simple one-step ionic liquid-based ultrasound-assisted dispersive liquid–liquid microextraction technique was coupled with high-performance liquid chromatography for the analysis of four pyrethroids in three kinds of traditional Chinese medicine oral liquid preparations: simotang oral liquid, kangbingdu oral liquid, and huaji oral liquid. The extraction parameters were examined to improve extraction efficiency. The optimum extraction conditions were 50 μL of 1-octyl-3-methylimidazolium hexafluorophosphate utilized as the extraction solvent and 800 μL of acetonitrile applied as the dispersive solvent. The extraction was assisted by ultrasonication for 8 min. The limits of detection for the four pyrethroids were within 0.007–0.024 mg L−1, and the limits of quantitation ranged between 0.023 and 0.080 mg L−1. The accuracy of the pyrethroid determination ranged from 80.1 to 106.4%. It was indicated that the proposed ionic liquid-based ultrasound-assisted dispersive liquid–liquid microextraction method had an easy operation and was accurate and environmentally friendly. This approach has potential for the analysis of pyrethroids in traditional Chinese medicine oral liquid preparations.

The recent years have been marked by the role of green tech innovation in decreasing carbon emissions worldwide to attain the carbon neutrality target. Despite many studies examining the nexus between the former and energy consumption, tech innovation's effects on CO2 releases have not been extensively researched, and the extant empirical findings are often contradictory. Also, a major concern regarding the available literature is the scarcity of papers that scan the impact of tourism on carbon emissions, even though the industry has a high potential to affect ambient air pollution. In this case, the evidence is mixed, and no consensus among academics on the relationships between the two. Therefore, this study seeks to investigate the relevance of green innovation and tourism in decreasing environmental damage in Thailand based on the bootstrapping ARDL causality model suggested by (McNown et al., 2018). This specification includes a new cointegration feature and conventional ARDL bounds tests, which increases the power of the t- and of the f-test and has several advantages, being more adequate for dynamic models with more than one explanatory variable. Our findings reveal that green innovation and tourism lead to lower environmental damage by reducing CO2 emissions, similar to foreign investments and that green tech innovation improves the environmental quality via lower carbon emissions.

Amino acids are widely distributed in biological fluids and involved in many biological processes, such as the synthesis of proteins, fatty acids, and ketone bodies. The altered levels of amino acids in biological fluids have been found to be closely related to several diseases, such as type 2 diabetes, kidney disease, liver disease, and cancer. Therefore, the development of analytical methods to measure amino acid concentrations in biological samples can contribute to research on the physiological actions of amino acids and the prediction, diagnosis and understanding of diseases. This review describes the analytical methods reported in 2012–2016 that utilized liquid chromatography and capillary electrophoresis coupled with ultraviolet, fluorescence, mass spectrometry, and electrochemical detection. Additionally, the relationship between amino acid concentrations and several diseases is also summarized.

Image representation has been intensively explored in the domain of computer vision for its significant influence on the relative tasks such as image clustering and classification. It is valuable to learn a low-dimensional representation of an image which preserves its inherent information from the original image space. At the perspective of manifold learning, this is implemented with the local invariant idea to capture the intrinsic low-dimensional manifold embedded in the high-dimensional input space. Inspired by the recent successes of deep architectures, we propose a local invariant deep nonlinear mapping algorithm, called graph regularized auto-encoder (GAE). With the graph regularization, the proposed method preserves the local connectivity from the original image space to the representation space, while the stacked auto-encoders provide explicit encoding model for fast inference and powerful expressive capacity for complex modeling. Theoretical analysis shows that the graph regularizer penalizes the weighted Frobenius norm of the Jacobian matrix of the encoder mapping, where the weight matrix captures the local property in the input space. Furthermore, the underlying effects on the hidden representation space are revealed, providing insightful explanation to the advantage of the proposed method. Finally, the experimental results on both clustering and classification tasks demonstrate the effectiveness of our GAE as well as the correctness of the proposed theoretical analysis, and it also suggests that GAE is a superior solution to the current deep representation learning techniques comparing with variant auto-encoders and existing local invariant methods.

In this paper, a novel method of QuEChERS-based extraction coupled with high-performance liquid chromatography has been developed for the simultaneous determination of ursolic acid (UA) and oleanolic acid (OA) in guava leaves. The QuEChERS-based extraction parameters, including the amount of added salt, vortex-assisted extraction time, and absorbent amount, and the chromatographic conditions were investigated for the analysis of UA and OA in guava leaves. Under the optimized conditions, the method showed good linearity over a range of 1–320 μg mL−1, with correlation coefficients above 0.999. The limits of detection of UA and OA were 0.18 and 0.36 μg mL−1, respectively. The intraday and interday precision were below 1.95 and 2.55%, respectively. The accuracies of the UA and OA determinations ranged from 97.4 to 111.4%. The contents of UA and OA in the guava leaf samples were 2.50 and 0.73 mg g−1, respectively. These results demonstrate that the developed method is applicable to the simultaneous determination of UA and OA in guava leaves.

We propose a technique for depth estimation with a monocular structured-light camera, i.e., a calibrated stereo set-up with one camera and one laser projector. Instead of formulating the depth estimation via a correspondence search problem, we show that a simple convolutional architecture is sufficient for high-quality disparity estimates in this setting. As accurate ground-truth is hard to obtain, we train our model in a self-supervised fashion with a combination of photometric and geometric losses. Further, we demonstrate that the projected pattern of the structured light sensor can be reliably separated from the ambient information. This can then be used to improve depth boundaries in a weakly supervised fashion by modeling the joint statistics of image and depth edges. The model trained in this fashion compares favorably to the state-of-the-art on challenging synthetic and real-world datasets. In addition, we contribute a novel simulator, which allows to benchmark active depth prediction algorithms in controlled conditions.

In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task., Comment: CVPR 2020

Dispersive liquid–liquid microextraction (DLLME) based on Fe3O4 magnetic nanoparticle-assisted in situ ionic liquid was developed as a novel method for the pretreatment of pyrethroid pesticides (flumethrin, bifenthrin and silafluofen) in urine samples. [N4,4,4,4]Cl and Na[N(CN)2] formed a ionic liquid ([N4,4,4,4][N(CN)2]), which provides an alternative to highly toxic solvents; moreover, high extraction recoveries and enrichment factors were achieved. The extraction parameters affecting the extraction recoveries were investigated. Under optimum conditions, the analytical method showed a good linear response, with correlation coefficient (R2) values ranging from 0.9994 to 0.9996. The enrichment factor, limit of detection and recovery were 108–125, 2.7–5.0 μg L−1 and 88.0–101.1%, respectively. Therefore, this green and simple method is suitable for the microextraction of pyrethroid pesticides in urine samples. It is valuable to establish a sensitive method for monitoring trace levels of pyrethroids, which could contribute to the use of rapid and green sample preparation procedures in urine samples.

Pyrethroid residues in traditional Chinese medicines have been a serious threat to the health and treatment of patients. However, because of the matrix complexity of traditional Chinese medicine, the detection of pyrethroid residues remains a challenge. Therefore, we developed a QuEChERS method coupled with high-performance liquid chromatography and ultraviolet detection (HPLC-UV) for the determination of pyrethroid pesticides in three kinds of traditional Chinese medicine oral liquid preparations, and we investigated and optimized the extraction conditions. The matrix effect was estimated in the organic solvent and the actual samples by comparing the slopes of calibration curves, and the results showed that the matrix effect is not significant when using the modified QuEChERS method. The pyrethroid pesticides could be completely separated in 30 min. The linear correlation coefficients were more than 0.999, and the recoveries of all the pyrethroid pesticides ranged from 87.2% to 104.8%. The intra-day precisions (n = 5) were 2.44&ndash, 4.62%, and the inter-day precisions (n = 5) were 1.06&ndash, 3.02%. Moreover, the limits of detection were in the range of 0.007&ndash, 0.018 ng mL-1, while the limits of quantitation were in the range of 0.022&ndash, 0.057 ng mL-1. This simple, low-cost, and highly sensitive analytical method can be a potential tool for the analysis of pyrethroid residues in traditional Chinese medicine oral liquid preparations.