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In this study, Ultrasound Needle-an improved minimally invasive ultrasonic horn device was used to explore its potential of synergizing with urokinase in enhancing clots lysis in an in vitro intracranial hematoma model.Ten milliliter bovine blood was incubated for 3 h at 37℃, and coagulated into clot to mimic intracranial hematoma in vitro. Ultrasound Needle was an improved ultrasonic horn with a fine tip (1.80 mm) and metallic sheath, and had a frequency of 29.62 kHz. The 10,000 IU urokinase was injected through the metallic sheath during the vibration of Ultrasound Needle tip to lyse the clots for 8 min under different working parameter settings (n = 8) to explore the influence of parameters Amplitude (%) and Duty (%) on clot lysis weight (WThe clot lysis weight WUltrasound Needle, an improved ultrasonic horn device, can mechanically damage the clot tissues and exhibit an excellent synergistic lysis effect with thrombolytic drugs. Therefore, Ultrasound Needle has great potential in providing a new minimally invasive strategy for rapid intracranial hematoma evacuation.

This paper addresses the guided depth completion task in which the goal is to predict a dense depth map given a guidance RGB image and sparse depth measurements. Recent advances on this problem nurture hopes that one day we can acquire accurate and dense depth at a very low cost. A major challenge of guided depth completion is to effectively make use of extremely sparse measurements, e.g., measurements covering less than 1% of the image pixels. In this paper, we propose a fully differentiable model that avoids convolving on sparse tensors by jointly learning depth interpolation and refinement. More specifically, we propose a differentiable kernel regression layer that interpolates the sparse depth measurements via learned kernels. We further refine the interpolated depth map using a residual depth refinement layer which leads to improved performance compared to learning absolute depth prediction using a vanilla network. We provide experimental evidence that our differentiable kernel regression layer not only enables end-to-end training from very sparse measurements using standard convolutional network architectures, but also leads to better depth interpolation results compared to existing heuristically motivated methods. We demonstrate that our method outperforms many state-of-the-art guided depth completion techniques on both NYUv2 and KITTI. We further show the generalization ability of our method with respect to the density and spatial statistics of the sparse depth measurements.

To explore the feasibility, efficacy, and safety of ultrasound-targeted microbubbles destruction (UTMD) assisted dual delivery of beta-amyloid (Aβ) antibody loaded by microbubbles (MBTwenty-seven APP/PS1 double transgenic mice (Tg mice) and 33 wild-type mice were used. Wild-type mice were insonated by diagnostic ultrasound with microbubbles (MB) for 5 min to observe the blood brain barrier (BBB) opening. The survival situation of engrafted NSCs crossing the opened BBB mediated by UTMD in Tg mice was evaluated by in vivo imaging system. We further explored the combination therapy effects of UTMD mediated Aβ antibody and NSCs dual delivery. Tg mice in each group were exposed to diagnostic ultrasound for 5 min once a week for four times, with MB, MBBBB was opened mediated by diagnostic ultrasound with MB, and the duration of opening was about 10 h. The transplanted NSCs survived in Tg mice for no more than 72 h. Compared with control group, the Tg mice in combined delivery of NSCs and Aβ antibody by UTMD group improved memory function and spatial learning with shorter latency to find the platform, longer distance traveled, and longer time spent in targeted quadrant, and more crossing times (p 0.05). Besides, the combination delivery group promoted the clearance of Aβ plaques compared with control group both in hippocampus (p 0.01) and cortex (p 0.05). Moreover, the expression of BDNF in combination delivery group was significantly higher than that in control group and ultrasound-mediated MB group (p 0.05). No significant change of SYN was observed in each group.UTMD assisted dual delivery of Aβ antibody and NSCs crossing the BBB into AD mice brain could help to clear Aβ plaques, increase the expression of BDNF, and restore the impaired neural function. This finding may offer potential insight into treatment of AD.

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.

Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging. In this paper, we propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures which ameliorates both issues. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities while explicitly modeling the aleatoric uncertainty inherent in the observations. Moreover, we formulate disparity estimation as a continuous problem in the image domain, allowing our model to query disparities at arbitrary spatial precision. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets. Our experiments demonstrate increased depth accuracy near object boundaries and prediction of ultra high-resolution disparity maps on standard GPUs. We demonstrate the flexibility of our technique by improving the performance of a variety of stereo backbones., Comment: CVPR 2021. Project Page: https://github.com/fabiotosi92/SMD-Nets

The unanticipated coronavirus disease 2019 (COVID-19) pandemic has hit global business heavily, disrupting the management of human resources across numerous industries. More than 500 articles (indexed in Scopus and the Web of Science) on the impact of the COVID-19 outbreak on emerging human resources issues and related practices were published from 1 January 2020 to 31 January 2021. In this study, we conduct a systematic literature review on emerging studies in the business and management field to explore what the emerging human resource issues are during the COVID-19 pandemic and propose related practices to solve these issues. The analysis of the published literature identifies nine main human resource issues across 13 industries. The findings of this study suggest that COVID-19 has enormous impact on conventional human resource management and requires the theoretical and empirical attention of researchers. The propositions nominate related human resource practices to deal with emerging human resources issues and identify several research venues for future studies in this field.

The COVID-19 pandemic and induced economic and social constraints have significantly impacted the confidence of both consumers and businesses. Despite that, comprehensive studies of the impact of the COVID-19 pandemic on the consumer and business sentiment are still lacking. Thus, in our research we aim to identify consumer and business confidence indicators’ reaction to the spread of the COVID-19 pandemic in the Eurozone, the United States, and China. For this purpose, we used the method of correlation–regression analysis. We chose the consumer-confidence index, manufacturing purchasing manager’s index, and services purchasing manager’s index as dependent variables, and the number of confirmed cases of COVID-19, the number of deaths caused by COVID-19, and the mortality rate of COVID-19 infections as independent variables. The results showed a relatively rapid and robust effect of COVID-19 in the short period, but longer-term results depended on the region and were not so unambiguous: in the case of the Eurozone, the spread of COVID-19 pandemic did not affect the consumer-confidence index (CCI) or, in the cases of the United States and China, affected this index negatively, the purchasing managers’ index (PMI) in the services sector was significantly negatively affected by the mortality risk of COVID-19 infection, and the impact on the purchasing managers’ index (PMI) in the manufacturing industry appeared to be mixed.

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.

This paper aims to (1) compare consumers’ preferences between individual products and bundles as well as (2) investigate some of the factors involved in bundle characteristics that may affect consumer’s preferences. Those factors are complementarity, price level, and discount level. An online survey developed by means of questionnaires were collected from the Portuguese population. Student’s t-tests were used to test the hypothesis formulated and to analyze the consumers’ preferences. The findings corroborate that in a scenario where the bundle does not offer any discounts, preference of individual products is higher. When a 20% discount is assigned to bundles, the overall preference for individual products is still superior. By offering a discount level of 45%, the overall preference for bundles becomes higher. The positive effect of complementarity bundles valuation is confirmed. This is the first approach to evaluate the preferences between bundles and individual products in the Portuguese market. The findings contribute to clarify the customer map within a Business Model Canvas. Furthermore, this paper analyzes the bundle complementarity and discount level effects simultaneously. Refereed/Peer-reviewed

Background : To explore the feasibility, efficacy and safety of ultrasound mediated microbubbles destruction（UMMD） assisted dual delivery of β-amyloid antibody loaded by microbubbles (MB Aβ ) and neural stem cells (NSCs) on Alzheimer’s disease（AD）. Methods : 27 APP/PS1 double transgenic mice and 33 wild-type mice were used. The dual delivery of β-amyloid antibody and NSCs group (US+MB Aβ +NSCs), single delivery of β-amyloid antibody group (US+MB Aβ ), US+MB group, Control group and Wild group, were involved in the experiment. MB Aβ or MB were injected via the tail vein, followed by NSCs or saline administration and exposed to ultrasound once a week for four times. The survival of NSCs was detected with the in vivo imaging method. Mice in each group were used for behavioral function evaluation and the pathology tests. Brain samples were used to detect β-amyloid deposition, BDNF and synaptophysin expression. Results : BBB was opened by UMMD with an opening time about 10 h. The transplanted NSCs survived in AD brain for no more than 72 h. The learning and spatial memory function was significantly improved in the US+MB Aβ +NSCs group, US+MB Aβ group came second. Immunochemistry results showed amyloid plaques reduction in the US+MB Aβ +NSCs group at the cortex and hippocampus. Higher level of BDNF was demonstrated in the US+MB Aβ +NSCs group than the US+MB Aβ group and the Control group with Western Blot and immunofluorescence examination, but synaptophysin remained no significant changes. Conclusions : UMMD assisted combined delivery of β-amyloid antibody and NSCs to AD mice brain can help to clear the Aβ peptide, increase BDNF level and restore the impaired neural function, which was superior to β-amyloid antibody delivery group. Therefore, the combined targeted delivery assisted by UMMD strategy may be a promising and safe method on treating AD.

Tumor angiogenesis is characterized by a defective, leaky and fragile microvascular construction, and microbubble-enhanced ultrasound (MEUS) with high-pressure amplitude is capable of disrupting tumor microvasculature and arresting blood perfusion. In this study, we tried to investigate whether the blood perfusion of a malignant tumor can be characteristically interrupted by combining microbubbles and diagnostic ultrasound (US). Twenty-nine Sprague-Dawley (SD) rats with subcutaneous Walker 256 tumors and seven healthy SD rats were included. Fifteen tumors were treated by MEUS, which combined constant microbubble injection and 20 episodes of irradiation by diagnostic US (i.e., acoustic radiation force impulse [ARFI] imaging). The other 14 tumors were treated by ARFI or sham US only. Seven skeleton muscles from healthy SD rats were also treated with MEUS, serving as the control. Contrast-enhanced ultrasound (CEUS) was performed before and after all treatments. The blood perfusion of the tumor MEUS group showed a significant drop immediately after treatment, followed by a quick, incomplete perfusion recovery within 10-20 min. The visual perfusion scoring result was consistent with the quantitative analysis by CEUS peak intensity. However, there were no significant perfusion changes in the tumor control groups or the muscle control group. Histologic examination found severe microvascular disruption and hemorrhage in the MEUS-treated tumors but not in the control groups. Therefore, the treatment combining diagnostic US and microbubbles can specifically decrease or interrupt the blood perfusion of Walker 256 tumors, which could be a potential new imaging method for diagnosing malignant tumors.

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.

In this paper, we present an online learning framework for traversable region detection fusing both appearance and geometry information. Our framework proposes an appearance classifier supervised by the sparse geometric clues to capture the variation in online data, yielding dense detection result in real time. It provides superior detection performance using appearance information with weak geometric prior and can be further improved with more geometry from external sensors. The learning process is divided into three steps: First, we construct features from the super-pixel level, which reduces the computational cost compared with the pixel level processing. Then we classify the multi-scale super-pixels to vote the label of each pixel. Second, we use weighted extreme learning machine as our classifier to deal with the imbalanced data distribution since the weak geometric prior only initializes the labels in a small region. Finally, we employ the online learning process so that our framework can be adaptive to the changing scenes. Experimental results on three different styles of image sequences, i.e., shadow road, rain sequence, and variational sequence, demonstrate the adaptability, stability, and parameter insensitivity of our weak geometry motivated method. We further demonstrate the performance of learning framework on additional five challenging data sets captured by Kinect V2 and stereo camera, validating the method’s effectiveness and efficiency.

© 2016 IEEE. Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. In recent years, there is a high exploitation of artificial intelligence algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. First, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Second, each layer of data are fed into a deep neural network for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained from all layers are fused based on confidence trees to maximize the overall confidence. Experimental results validate the effectiveness of our end-to-end place classification framework in which both the multilayer structure and the graph regularization promote the classification performance. Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.

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

Most of the top performing action recognition methods use optical flow as a “black box” input. Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better. In particular, we investigate the impact of different flow algorithms and input transformations to better understand how these affect a state-of-the-art action recognition method. Furthermore, we fine tune two neural-network flow methods end-to-end on the most widely used action recognition dataset (UCF101). Based on these experiments, we make the following five observations: (1) optical flow is useful for action recognition because it is invariant to appearance, (2) optical flow methods are optimized to minimize end-point-error (EPE), but the EPE of current methods is not well correlated with action recognition performance, (3) for the flow methods tested, accuracy at boundaries and at small displacements is most correlated with action recognition performance, (4) training optical flow to minimize classification error instead of minimizing EPE improves recognition performance, and (5) optical flow learned for the task of action recognition differs from traditional optical flow especially inside the human body and at the boundary of the body. These observations may encourage optical flow researchers to look beyond EPE as a goal and guide action recognition researchers to seek better motion cues, leading to a tighter integration of the optical flow and action recognition communities.

Nonnegative matrix factorization (NMF) has been proved to be a powerful data representation method, and has shown success in applications such as data representation and document clustering. However, the non-negative constraint alone is not able to capture the underlying properties of the data. In this paper, we present a framework to enforce general subspace constraints into NMF by augmenting the original objective function with two additional terms. One on constraints of the basis, the other on preserving the structural properties of the original data. This framework is general as it can be used to regularize NMF with a wide variety of subspace constraints that can be formulated into a certain form such as PCA, Fisher LDA and LPP. In addition, we present an iterative optimization algorithm to solve the general subspace constrained non-negative matrix factorization (GSC NMF). We show that the resulting subspace has enriched representation power as shown in our experiments.

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.

Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (e.g., TSDF) from which 3D surface meshes must be extracted in a post-processing step (e.g., via the marching cubes algorithm). In this paper, we investigate the problem of end-to-end 3D surface prediction. We first demonstrate that the marching cubes algorithm is not differentiable and propose an alternative differentiable formulation which we insert as a final layer into a 3D convolutional neural network. We further propose a set of loss functions which allow for training our model with sparse point supervision. Our experiments demonstrate that the model allows for predicting sub-voxel accurate 3D shapes of arbitrary topology. Additionally, it learns to complete shapes and to separate an object's inside from its outside even in the presence of sparse and incomplete ground truth. We investigate the benefits of our approach on the task of inferring shapes from 3D point clouds. Our model is flexible and can be combined with a variety of shape encoder and shape inference techniques.

© 2017 IEEE. Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera. Although those platforms do not have sensors for 3D depth sensing capability, knowledge of depth is an essential part in many robotics activities. Therefore, recently, there is an increasing interest in depth estimation using monocular images. As this task is inherently ambiguous, the data-driven estimated depth might be unreliable in robotics applications. In this paper, we have attempted to improve the precision of monocular depth estimation by introducing 2D planar observation from the remaining laser range finder without extra cost. Specifically, we construct a dense reference map from the sparse laser range data, redefining the depth estimation task as estimating the distance between the real and the reference depth. To solve the problem, we construct a novel residual of residual neural network, and tightly combine the classification and regression losses for continuous depth estimation. Experimental results suggest that our method achieves considerable promotion compared to the state-of-the-art methods on both NYUD2 and KITTI, validating the effectiveness of our method on leveraging the additional sensory information. We further demonstrate the potential usage of our method in obstacle avoidance where our methodology provides comprehensive depth information compared to the solution using monocular camera or 2D laser range finder alone.

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.

Octahedral Cu 2 O particles were prepared on an indium-tin oxide glass via electrodeposition and were employed in the catalytic degradation of p -nitrophenol in the presence of H 2 O 2 . Under irradiation of a warm visible-light source, tungsten halogen lamp, Cu 2 O particles not only acted as a photocatalyst, but might also act as a thermal-catalyst to induce the decomposition of H 2 O 2 and produce O 2 at higher temperatures. The photogenerated electrons and holes could react with H 2 O 2 , O 2 , and H 2 O to produce abundant OH radicals, resulting in the effective oxidation of p -nitrophenol. High-performance liquid chromatography measurements of degradation intermediates showed that p -nitrophenol was first decomposed into hydroquinone and benzoquinone and then mineralized. The degradation efficiency was dependent on electrodeposition time, light intensity, H 2 O 2 amount, and solution temperature. This catalyst could be easily recycled and used in the efficient degradation of other phenolic compounds.

Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms.

We introduce a novel RGB-D descriptor called local ordinal intensity and normal descriptor (LOIND) with the integration of texture information in RGB image and geometric information in depth image. We implement the descriptor with a 3-D histogram supported by orders of intensities and angles between normal vectors, in addition with the spatial sub-divisions. The former ordering information which is invariant under the transformation of illumination, scale and rotation provides the robustness of our descriptor, while the latter spatial distribution provides higher information capacity so that the discriminative performance is promoted. Comparable experiments with the state-of-art descriptors, e.g. SIFT, SURF, CSHOT and BRAND, show the effectiveness of our LOIND to the complex illumination changes and scale transformation. We also provide a new method to estimate the dominant orientation with only the geometric information, which can ensure the rotation invariance under extremely poor illumination.

In this paper, we present a novel learning framework for traversable region detection. Firstly, we construct features from the super-pixel level which can reduce the computational cost compared to pixel level. Multi-scale super-pixels are extracted to give consideration to both outline and detail information. Then we classify the multiple-scale super-pixels and merge the labels in pixel level. Meanwhile, we use weighted ELM as our classifier which can deal with the imbalanced class distribution since we only assume that a small region in front of robot is traversable at the beginning of learning. Finally, we employ the online learning process so that our framework can be adaptive to varied scenes. Experimental results on three different style of image sequences, i.e. shadow road, rain sequence and variational sequence, demonstrate the adaptability, stability and parameter insensitivity of our method to the varied scenes and complex illumination.

Scene classification is a fundamental perception task for environmental understanding in today's robotics. In this paper, we have attempted to exploit the use of popular machine learning technique of deep learning to enhance scene understanding, particularly in robotics applications. As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples. Inspired by human scene understanding based on object knowledge, we address the problem of scene classification by encouraging deep neural networks to incorporate object-level information. This is implemented with a regularization of semantic segmentation. With only 5 thousand training images, as opposed to 2.5 million images, we show the proposed deep architecture achieves superior scene classification results to the state-of-the-art on a publicly available SUN RGB-D dataset. In addition, performance of semantic segmentation, the regularizer, also reaches a new record with refinement derived from predicted scene labels. Finally, we apply our model trained on SUN RGB-D dataset to a set of images captured in our university using a mobile robot, demonstrating the generalization ability of the proposed algorithm.