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Accurate optical flow estimation with the frequency-domain regularization is a challenging problem in computer vision. In this paper, we solve this issue by introducing a novel optical flow method related to the frequency domain that uses TV-wavelet regularization. Specifically, we regard TV-wavelet regularization as a filtering process. After wavelet transform for optical flow field, we firstly remove outliers by performing a threshold operation. Then, we make up for lost motion information (such as flow edges and important motion details) determined by these missing or damaged wavelet coefficients by adding TV-wavelet coefficients that are obtained from transform spectrum of the prior flow geometrical features, which are controlled by the image structures. By combining the advantages of total variation to recover geometric structures with the strengths of wavelet representation to remove outliers, the proposed method significantly outperforms the current frequency-domain optical flow methods in removing outliers, preserving sharp flow edges, and restoring important motion details. It also shows competitive optical flow evaluation results on the challenging MPI-Sintel, Kitti, and Middlebury datasets.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder which is caused by the interplay of genetic and environmental risk factors such as second-hand smoke (SHS). The association between postnatal exposure to SHS and ADHD risk in children was still inconclusive. We performed a systematic review and meta-analysis to explore the definite association. We searched for relevant studies from PubMed, Embase, Ovid, and Web of Science databases up to January 2020. We used random effect models to calculate pooled odds ratio (OR) with 95% confidence interval (CI). Subgroup analyses and sensitive analyses were also performed to solve the heterogeneity. According to our inclusion criteria, 9 studies including 6 cross-sectional studies, 2 cohort studies, and 1 case-control study were included in the final analysis. Postnatal exposure to SHS increased the risk of ADHD in children (OR: 1.60; 95% CI: 1.37-1.87). Children who exposed to SHS were found a slight risk for conduct problems (OR: 1.33, 95% CI: 1.00-1.77). Among the studies which used cotinine as a biomarker for SHS exposure, a lower pooled OR (OR = 1.16, 95%CI = 1.01, 1.33) was observed between cotinine and ADHD in children. Our meta-analysis results suggested that SHS exposure may be a risk factor for ADHD. We also found that SHS exposure may be associated with some adverse behavioral outcomes. More prospective studies should be conducted to confirm the relationship between SHS exposure and ADHD in children.

Motion estimation for complex fluid flows via their image sequences is a challenging issue in computer vision. It plays a significant role in scientific research and engineering applications related to meteorology, oceanography, and fluid mechanics. In this paper, we introduce a novel convolutional neural network (CNN)-based motion estimator for complex fluid flows using multiscale cost volume. It uses correlation coefficients as the matching costs, which can improve the accuracy of motion estimation by enhancing the discrimination of the feature matching and overcoming the feature distortions caused by the changes of fluid shapes and illuminations. Specifically, it first generates sparse seeds by a feature extraction network. A correlation pyramid is then constructed for all pairs of sparse seeds, and the predicted matches are iteratively updated through a recurrent neural network, which lookups a multi-scale cost volume from a correlation pyramid via a multi-scale search scheme. Then it uses the searched multi-scale cost volume, the current matches, and the context features as the input features to correlate the predicted matches. Since the multi-scale cost volume contains motion information for both large and small displacements, it can recover small-scale motion structures. However, the predicted matches are sparse, so the final flow field is computed by performing a CNN-based interpolation for these sparse matches. The experimental results show that our method significantly outperforms the current motion estimators in capturing different motion patterns in complex fluid flows, especially in recovering some small-scale vortices. It also achieves state-of-the-art evaluation results on the public fluid datasets and successfully captures the storms in Jupiter’s White Ovals from the remote sensing images.

We present a novel optical flow estimation framework that reinterprets most popular algorithms (e.g., the Horn–Schunck model, PDE-based models, TV-based models, and nonlocal-based models) from an iterative filtering perspective. Different regularizers in the related optical flow estimation algorithms can be achieved by adopting different filtering operations. The key merit of the proposed framework is that a variety of filtering models and corresponding optimization strategies, which are used in image processing, can be directly utilized in the regularization term, leading to a convenient way of designing appropriate optical flow estimation algorithms. Under the proposed filtering framework, we demonstrate easily designing optical flow estimation algorithms with respect to translation consistency, rotation consistency, and divergence consistency constraints by adopting different spatial filters, respectively. We also derive a novel optical flow estimation algorithm with 3D filtering-based model for the regularization term, which makes use of nonlocal self-similarity and sparse characteristic of optical flow field. Benefited from the advantages of patch-based nonlocal sparse regularizer, the derived algorithm can remove outliers while preserving sharp flow edges and important motion details. At present, the proposed algorithm achieves a high ranking on the challenging MPI-Sintel dataset and shows good performance on the Kitti flow 2015 and Middlebury datasets.

This review provides a systematical description of the inverse heat transfer solutions applied to global heat-flux measurements using temperature-sensitive-coating techniques in hypersonic wind tunnels. The analytical inverse heat transfer solutions for a two-layer structure (typically a polymer sensor coating on a base) are focused to elucidate the effects of the relevant parameters including the polymer and base thicknesses, the thermal properties of materials, and the convective heat transfer at the base backside. Then, the numerical inverse heat transfer method is developed by incorporating the temperature-dependencies of the thermal properties in heat-flux calculation. Furthermore, correcting the lateral heat conduction effect is formulated as an image deconvolution problem based on a convolution-type integral equation with a Gaussian kernel. Other relevant topics are also discussed, including determining the non-uniform paint thickness distribution, in-situ correction for the effect of the temperature-dependent diffusivity, and in-situ calibration for the thermal penetration parameter of the polymer coating. Examples are given to demonstrate the applications of these inverse solutions in temperature-sensitive-paint (TSP) measurements.

Background Idiopathic scoliosis (IS) affects patients’ quality of life, yet there have been few reports of its morphology and epidemiological study in the southeast region of China. The aim of this study is to access the curve characteristics, prevalence, and factors associated with IS in Chaozhou city. Methods A cross-sectional study was performed in 2018, in which scoliosis screening was conducted among 5497 primary school students in Chaozhou city. Then, a case-control study based on the screening involving 2547 children was followed for the exploration of the associated factors. The questionnaires covering demographic characteristics, postural habits, cognition and self-sensation of scoliosis, and physical conditions were addressed for the investigation. ORs with 95%CIs were calculated based on logistic regression analysis to evaluate the factors associated with scoliosis. Results The prevalence of IS among primary school students was 6.15% in Chaozhou city, with 4.04% for males and 8.71% for females. The average Cobb angle was 15° (range 8 to 37°). Multiple logistic regression analysis suggested that female (OR=2.45), BMI (OR=0.67), having myopia (OR=1.49), self-sensation of scoliosis with symptoms (OR=5.52), insufficient sleep time (OR=2.65, 3.33), and less exercise time (OR=7.09, 7.29) were significantly associated with IS. Conclusions The prevalence of IS among primary school students in Chaozhou was at an average level, and it was significantly higher in females than in males. Lower body mass, having myopia, insufficient sleep time, and lower physical activity were associated with IS.

In this paper, we proposed a novel variational optical flow model with L 0 norm regularization, which uses a sparse flow gradient counting scheme, and can globally control how many non-zero flow gradients are preserved to recover important motion structures in a sparsity-control manner. It is particularly effective for enhancing major flow edges while eliminating a manageable degree of low-amplitude motion structures to control smoothing and reduce oversegmentation artifacts. Different from other edge-preserving smoothing regularizers, it does not depend on local motion features, but locates important flow edges globally. It will not cause edge blurriness due to avoiding local filtering or average operation, and can preserve salient motion structures, even small-scale motion structures with high contrast can be preserved remarkably well. Benefited from the advantages of L 0 norm regularization, the proposed optical flow method shows outstanding performance in sharpening major flow edges, flattening insignificant motion details to control smoothing and reduce oversegmentation artifacts while preserving fine-scale motion structures with high contrast. It also achieves good performance on the Middlebury dataset.

Polybrominated diphenyl ethers (PBDEs) are a class of widely used flame retardants, and their residue in the environment may threaten the ecosystem and human health. The neurodevelopmental toxic effects of PBDEs have been verified in previous studies, but the mechanisms are still unclear. Behavioral analysis and transcriptomics were performed in this study to assess the neurodevelopmental toxic effects of PBDEs on zebrafish embryos and larvae, and the potential mechanisms. The embryos were collected after fertilization and exposed to control (0.05% DMSO), 10, 50, 100 (ug/L) 2, 2′, 4, 4′-tetrabromodiphenyl ether (BDE-47) for 7 days. The locomotion parameters of larvae were recorded and analyzed by a behavioral analysis system (EthoVision XT, Noldus). Enrichment of functions and signaling pathways of differentially expressed genes (DEGs) were analyzed by GO and DAVID database. The comparison with the control group showed adverse developments such as low hatching rate, high mortality rate, alterative heart rate, and abnormal spontaneous tail coiling frequency of embryos (24hpf). For the zebrafish larvae, behavioral analyses results suggested decreased activities and movements of the treatment in the light-dark period at 120, 144 and 168hpf, especially the 50 and 100μg/L groups. The affected functions included steroid hormone regulation, neuro regulation, circadian regulation, cardioblast differentiation, immune-related regulation. The enrichment of KEGG pathways were Hedgehog signaling (Shh), Toll-like receptor signaling, FoxO signaling, and Steroid biosynthesis pathway. Hedgehog signaling pathway was further verified via RT-qPCR for its major role in the development of neurogenesis. The mRNA levels of Shh pathway indicated the inhibition of Shh signal in our study since shha, patched1, gli1 and gli2 genes were significantly down-regulated. In summary, PBDEs might influence the neurodevelopment of zebrafish in the early life stage by multiple toxic signaling pathways alteration.

Background Bisphenol A (BPA) is a well-known xenobiotic endocrine disrupting chemical, with estrogenic activity and many other potential biological effects. Although multiple toxicities have been reported for BPA, molecular mechanisms underlying the transgenerational toxic effects of BPA are still underestimated. Methods Parental F0 fish were exposed to 1.0 μM BPA or control (0.1% DMSO, v/v) for 7 days. Eggs (F1) were collected and kept in control medium until 4.5 or 120 h post fertilization (hpf). RNA sequencing (RNA-seq) was conducted on embryos and larvae, to discover differentially expressed genes (DEGs), and then KEGG pathway, GO enrichment and GSEA were performed to interpret functional ontology. Histopathology was performed to explore the morphological and structural alterations in liver tissues of zebrafish larvae (120 hpf) after parental BPA exposure. Results Parental BPA exposure induced global transcriptomic changes in zebrafish embryos and larvae. For embryos, epigenetic regulation genes were decidedly affected, highlighted epigenotoxicity might involve in the transgenerational effects during embryogenesis and early development. By further investigation on its delayed effects, our RNA-Seq data of larvae suggested ROS metabolic process, apoptosis, p53 and MAPK signaling pathway were concentrated, indicating defensive cellular processes still involved in protecting against BPA toxicity. Furthermore, parental BPA-treated larvae manifested hepatic injury by histopathological analysis. Conclusions Parental BPA exposure led to global transcriptomic changes involved in epigenetic regulation, oxidative stress, apoptosis and DNA damage of offspring. These findings advanced the field of the parental-mediated subsequent generational toxic effects of BPA.

This paper proposes a variational formulation with a smoothness constraint for extraction of pressure from two-dimensional velocity field. The method of characteristics is first described as a reduced case to obtain pressure data by a line integral along a given line. Then, a global optimization method is developed, and the Euler–Lagrange equation is obtained to determine a pressure field from velocity data in a domain with suitable boundary conditions. Numerical algorithms are developed to solve the Euler–Lagrange equation with suitable boundary conditions. The accuracy of the developed methods and the effects of the relevant parameters on extraction of pressure are evaluated on a simulated two-dimensional oblique Hiemenz flow. Subsequently, in an experimental case, pressure fields near a freely flying hawkmoth are estimated from high-resolution velocity fields that are obtained by applying the optical flow method to high-speed Schlieren photography images. Pressure field near the hawkmoth wingtip obtained by using (a) Direct-QR method. And (b) iterative method.

The one-dimensional unsteady heat transfer of a polymer layer on a finite base is studied. A numerical inverse method is developed to calculate heat flux from surface-temperature images obtained using temperature-sensitive coating particularly in high-enthalpy wind tunnels. It is found that the effect of the finite thickness of the base is significant as time increases such that the semi-infinite base assumption made in many methods is no longer accurate in heat-flux calculation. The method developed in this work can take into account the effects of the finite thickness of a base and the temperature-dependencies of the thermal properties of the materials. This method has been validated through simulations and processing the experimental temperature-sensitive-paint (TSP) images.

Fast and accurate optical flow estimation is a challenging problem in computer vision. In this paper, we present a novel model to solve the optical flow problem by combining the strengths of the Split Bregman method with the advantages of an efficient variational framework. It allows us to employ different regularization tensors for the Split Bregman regularizer to preserve motion discontinuities. Simultaneously, a novel nonlocal Split Bregman method with adaptive support weights is also developed for the smoothness regularization to preserve motion details, repel outliers, enhance contrast, and reduce motion blurring and staircase effect. The proposed algorithm shows significant improvements in preserving sharp flow edges and important motion details. Most of all, it requires only a few iterations to achieve fast convergence and runs faster than the classic TV-L1 flow method. Our method significantly outperforms the current state-of-the-art methods on the challenging MPI-Sintel dataset and shows good performance on the Kitti flow 2015 and Middlebury datasets.

An analytical inverse heat transfer solution is given for calculating surface heat flux from temperature-sensitive coating measurements on a base with a finite thickness, which is particularly relevant to high enthalpy hypersonic wind tunnel experiments. This solution is validated through simulations for several time histories of the heat flux imposed on the surface of the base, focusing on the effects of the relevant parameters including the control parameter in the inverse Laplace transform, base thickness and convective heat transfer on the backside of the base. Further, this solution is used to extract the heat-flux fields from temperature-sensitive-paint images obtained on a flat surface of an aluminum base in a transonic obliquely impinging jet. The experimental data exhibit the self-similar evolution of the surface temperature and heat-flux fields. The Nusselt number distribution evaluated by using this analytical method is consistent with the previous results.

Object To study the outcomes of GnRHa on final adult height in Chinese idiopathic central precocious puberty (ICPP) girls and the involved factor(s) that can predict height gain. Methods We conducted a retrospective analysis on 10 years of data obtained from three clinical hospitals from January 2005 to March 2015, and 101 girls with ICPP, who received GnRHa therapy for more than six months and already reached their adult height were enrolled. Results Height, bone age, midparent height, HtSDS, sexual development, therapy duration and predicted adult height(PAH)at start and end of GnRHa, and the final adult height(FAH) were recorded and calculated. Their PAH significantly increased at end of GnRHa (158.4±6.00cm), compared to that at the start of GnRHa(153.1±5.37cm) (P0.05). After GnRHa therapy, most of the ICPP girls reached their midparent height compared to that at start of GnRHa(P0.05). The percentage of adult short stature decreased and those reached midparent height significantly increased after GnRHa therapy, compared to that at start of GnRHa(60.6% vs.30.4% and 67.85% vs. 94.64%, respectively, P

Limited-angle tomography has gained much interest in late years Nevertheless, image reconstruction from incomplete projections is a classic ill-posed issue in the field of computational imaging. In this paper, we propose a scheme based on the sparsifying operators and approximation of e0-minimization. Our framework includes two main components, one for a sparsifying operator, and one for learning the scheme parameters using e0-minimization from insufficient computed tomography data. Thus, the proposed scheme is capable of recovering high quality reconstructions at a range of angles and noise. Compared to the total-variation (TV) regularized reconstruction scheme, σ-u scheme and ATV (Anisotropic Total Variation) scheme, validations using Shepp-Logan phantom computed tomography data demonstrate the significant improvements in SNR and suppressed noise and artifacts.

Background: The outbreak of Coronavirus Disease 2019 (COVID-19) has seriously affected people’s life. The main aim of our investigation was to determine the interactive effects of disease awareness on coping style among Chinese residents during the COVID-19 pandemic. Methods: A total of 616 Chinese residents from 28 provinces were recruited to participate in this investigation. A questionnaire was used to collect demographic characteristics, cognition of COVID-19, and disease-related stress sources. Coping styles were assessed via the Simplified Coping Style Questionnaire (SCSQ). Results: The survey showed that the main source of information on COVID-19 was different in relation to gender, age, educational level, and occupation (p &lt, 0.001). People’s knowledge of the disease, preventive measures, and stress factors were different in relation to demographic characteristics (p &lt, 0.001). Compared with the baseline values, the scores of positive coping and negative coping based on SCSQ in relation to gender, age, educational level, and occupation were statistically significant (p &lt, 0.001, except for participants older than 60 years). Different educational levels corresponded to statistical significant differences in positive coping (p = 0.004) but not in negative coping. Conclusions: During the pandemic, people with different characteristics had different levels of preventive measures’ awareness, which influenced their coping styles. Therefore, during public health emergencies, knowledge of prevention and control measures should be efficiently provided to allow more effective coping styles.

Background: The outbreak of Corona Virus Disease 2019 (COVID-19) might affect the psychological health of population, especially medical workers. We aimed to investigate the impact of the COVID-19 pandemic on emotional and cognitive responses and behavioral coping among Chinese residents. Methods: An online investigation was run from 5 February to 25 February 2020, which recruited a total of 616 Chinese residents. Self-designed questionnaires were used to collect demographic information, epidemic knowledge and prevention of COVID-19 and characteristics of medical workers. The emotional and cognitive responses were assessed via the Symptom Check List-30 (SCL-30) and Yale&ndash, Brown Obsessive Compulsive Scale (Y-BOCS). Behavioral coping was assessed via Simplified Coping Style Questionnaire (SCSQ). Results: In total, 131 (21.3%) medical workers and 485 (78.7%) members of the general public completed the structured online survey. The structural equation models showed that emotional response interacted with cognitive response, and both emotional response and cognitive response affected the behavioral coping. Multivariate regression showed that positive coping enhanced emotional and cognitive responses, while negative coping reduced emotional and cognitive responses. The emotional response (depression, anxiety and photic anxiety) scores of the participants were higher than the norm (all p &lt, 0.001), in particular, the panic scores of members of the general public were higher than those of medical workers (p &lt, 0.05), as well as the cognitive response (paranoia and compulsion). Both positive and negative coping scores of the participants were lower than the norm (p &lt, 0.001), and the general public had higher negative coping than medical workers (p &lt, 0.05). Conclusion: During the preliminary stage of COVID-19, our study confirmed the significance of emotional and cognitive responses, which were associated with behavioral coping and significantly influenced the medical workers and the general public&rsquo, s cognition and level of public health emergency preparedness. These results emphasize the importance of psychological health at times of widespread crisis.

Automatic ship detection of synthetic aperture radar (SAR) images has been widely used in maritime surveillance. SAR images have the characteristics of all-weather, all-day detection. Therefore, many object detection methods ranging from traditional to deep learning techniques have been proposed. However, the objects in large-scale remote sensing images are relatively small, and objects are often appeared at different scales. What’s more, the current ship detection methods are insensitive to small-scale vessels. To solve these problems, a novel multi-scale ship detection method based on a Multi-scale Faster R-CNN network in SAR images is proposed in this paper. Firstly, a multi-scale network is used to decompose the SAR images into a pyramid structure and extract the features. Then, the region proposal network (RPN) is performed using the feature map for each layer to get the proposals that contains ship targets. Finally, these proposals are fed to the classification and regression network to obtain the final detection results. Multi-scale Faster R-CNN achieves the mean average precision(mAP) score 0.986 on the dataset of SAR-Ship-Dataset, which indicates that the proposed method has high detection accuracy and low missing rate.

Due to the numerous important applications of video surveillance and monitoring, video object tracking has been an active research topic in the last decade. This paper makes a survey of approaches to high quality object tracking by looking at theoretical backgrounds and practical results, which are categorized into four groups. The principle, the evolution processes and the latest progresses of these approaches are identified to form a conclusion for future directions of object tracking algorithms.

In recent years, there is a growing interest in the research of color image de-noising algorithms. A tight framelet is a wavelet frame constructed by multiresolution analysis. The properties of tight framelet and its applications on gray image denoising have been well studied. Our goal is to explore the power of tight framelet to de-noise color images. We analyze the relationship between gray images and color images. The frame-based gray image de-nosing scheme is extended to color image de-noising. Experimental results of color image de-noising show that the new method has a better performance than AMF approach.

This paper describes a numerical inverse method used in temperature-sensitive-paint measurements for quantitative global heat flux diagnostics in hypersonic tunnels. An iterative algorithm is developed to solve the one-dimensional inverse heat transfer problem for unsteady heat flux into a polymer layer (temperature-sensitive paint) on a semi-infinite base, where the temperature dependencies of the thermal properties of materials are taken intoaccount.Surfaceheat fluxdistributionsonconemodelsarecalculatedbyusingthisalgorithmfromtemperaturesensitive-paint images acquired in hypersonic wind tunnel testing. Nomenclature a = thermal diffusivity c = specific heat I = luminescent intensity k = thermal conductivity L = polymer thickness qs = surface heat flux qs� 1D� = surface heat flux obtained by using the onedimensional method T = temperature Tin = initial temperature t = time x, z = coordinates on surface y = coordinate in normal direction to surface

An efficient and robust meter readings location and recognition algorithm plays a very important role in remote meter reading systems and automatic meter inspection systems. A novel and efficient electric meter readings automatic location and recognition method is presented in this study. It includes the following three steps: coarse-to-fine detection of meter readings, projection-based character segmentation and BP neural network recognition. The algorithm was tested on HUALI, KEYUAN, JINLING, LANJIER and SANXING electric meter images. Experimental results show that the proposed method has satisfying performance and good robustness.

This paper describes the analytical method used for temperature-sensitive-paint measurements to obtain quantitative global heat flux diagnostics in hypersonic tunnels. An exact inverse solution of the one-dimensional unsteady heat conduction equation for a polymer layer (temperature-sensitive paint or temperature-sensitive paint and an insulator layer) on a base of any material is obtained to calculate heat flux into the polymer surface as an explicit function of a time-dependent surface temperature change measured by temperature-sensitive paint. In addition, an analytical solution of the three-dimensional unsteady heat conduction equation is given to consider the effect of the lateral heat conduction. Simulations and experiments are conducted to examine the analytical method and assess the relevant factors regarding the measurement uncertainty.

Railway condition monitoring is one of crucial factors to ensure safe and cost-effectiveness of train operation. A fiber Bragg grating sensors based real-time train wheel condition monitoring system has been developed and reported by our team. In the system, the condition index (CI) is generated to indicate wheel condition. To further verify the system is reliable. This paper reports, as case studies, the two-parameter Weibull distribution model is used to analyze the CI of nine train cars, and proves that the CI can well indicate the wheel condition. Moreover a discussion of cost and reliability for wheel maintenance, also reported in the paper, can help railway operators to achieve the best balance between the cost and the reliability.