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AbstractEconomic policy uncertainty has prominent effects on investment and output. Using unique transaction-level data in China, this paper explores the impact of economic policy uncertainty on land transaction premiums in China. On average, a 1% increase in the policy uncertainty level reduces land transaction premiums by 3.907 percentage points. The impact of policy uncertainty is more profound for firms with tighter financial constraints and land located in cities with lower rent-to-price ratios. The influence of policy uncertainty becomes weaker after the anti-corruption campaign in China, especially in provinces scrutinised by the central inspection teams, implying that improvements in the political environment alleviates the impact of policy uncertainty.

Land seismic data are often obscured by noise, severely affecting the accuracy of subsequent seismic imaging and interpretation. Dictionary learning (DL) is an effective method for noise suppression. However, finding a fast DL method that is suitable for weak signals and can suppress multiscale strong noise is still a hot topic. In this article, we introduce a noise suppression method that combines DL with fast adaptive empirical mode decomposition (FABEMD). We leverage the advantages of FABEMD in multiscale signal decomposition, along with the efficient sparse representation capabilities of DL, to achieve noise suppression for low signal-to-noise ratio (LSNR) seismic signals. We group bidimensional intrinsic mode functions (IMFs) based on their cross correlation coefficients and train dictionaries for components using the sequential generalization K-means method, enhancing computational efficiency and adaptability. Numerical examples using both synthetic and field data validate the practicality and versatility of the proposed method, indicating its improved performance in denoising compared to $f-x$ EMD, BEMD, and traditional DL methods.

Traffic sensor data are prone to malfunctions caused by various factors such as manufacturing defects, harsh environmental conditions, improper installation, and maintenance. While fault data detection is a well-established practice in many engineering domains, its application for quality control of traffic sensor data has not received due attention. This paper introduces a symmetric contrastive learning approach that employs a triplet network with an efficient sampling strategy, coupled with a novel cross-attention-boosted loss function for network training. To better discern the faults observed in the time-series of traffic sensor data, we employ continuous wavelet transformation (CWT) as a preprocessing step to obtain time–frequency wavelet image representation. These CWT images are used to pretrain the triplet encoder. To enhance training efficiency, we adopt an anchor-centered sampling strategy, referred to as symmetric contrastive sampling. This involves sampling a normal day of time-series traffic data as the anchor and generating three positive and three negative examples based on the domain knowledge. The loss is computed from the nine anchor-centered possible permutations of the triplet (anchor, negative, positive). This symmetric sampling approach facilitates direct contrast between positive and negative examples derived from the same anchor, leading to stronger contrastive signals for faster and more stable learning process. In comparison with traditional triplet and Siamese networks, as well as a classic threshold-based method, our proposed approach shows superior performance in detecting faulty data sequences. The experimental results demonstrate an impressive accuracy of 97.6%, precision of 97.5%, recall of 97.7%, and an F1-score of 97.6%.

The presence of source-related noise, characterized by exceptionally large amplitudes, poses a significant challenge in seismic data processing, impeding the accurate recovery of the effective signal within overlapping regions. In response, we introduce an adaptive time-frequency denoising method tailored to mitigate this issue. First, the seismic data is horizontally sorted, and exponential function fittings are applied to exploit amplitude difference information embedded in the dataset. Then, the curvatures of the exponential functions are computed, and their maximal values are chosen to establish a dynamic threshold curve, offering an estimate for the range of abnormal traces from shallow to deep regions. Next, further refinement of the dynamic threshold curve involves iterative adjustments based on the standard deviation of the curvature functions, resulting in a more precise separation between signal and noise traces. Finally, leveraging the optimized dynamic threshold curve, adaptive threshold value computations are performed at each frequency slice and subsequently applied through soft thresholding to effectively reduce the impact of strong amplitude noise. Numerical examples involving synthetic and field data demonstrate the effectiveness of the proposed method in attenuating strong source-related noise, surpassing the performance of traditional f-k filter, nonconvex (NC) threshold, and static threshold methods.

Least-squares migration (LSM) aims to seek the best-fit solution for subsurface reflectivity with high image resolution and balanced amplitudes by minimizing the mismatching between synthetic and observed seismic data. It can be implemented either in data domain or in image domain. Data-domain LSM iteratively updates reflectivity using gradient-based algorithms. However, it requires expensive computation cost to converge to a good solution, which is still challenging for large-scale datasets under current computational capacity. Point-spread function (PSF) deconvolution is an efficient and accurate image-domain LSM approach to reduce migration artifacts caused by the limited aperture and the finite frequency wavelet and improve image resolution. However, seismic velocity models have millions of grid points, which makes it prohibitively expensive to directly compute the PSF and its deconvolution operator. To obtain high-resolution images and reduce computing cost, we develop a deep-learning-based method to directly approximate the deconvolution operator of each separated PSF. First, we calculate migrated image and PSFs using one pass of seismic modeling and traditional adjoint migration. Next, we train conditional generative adversarial networks (cGANs) by using the migrated images, PSFs, and migration velocity as the input and the true reflectivity as the labeled data. Finally, the trained network is applied to the migrated images, PSFs, and migration velocity of test datasets to predict the reflectivity model. With the well-trained cGANs, we can predict high-quality LSM images efficiently and save considerable computational cost. Numerical examples for synthetic models and field data demonstrate that the proposed method can accurately predict PSF deconvolution operators and provide high-quality deblurred LSM images with significantly reduced computational and memory costs.

This paper investigates the determinants and the time pattern of the subjective well-being of private business owners. We find that both personal and enterprise-level factors have a significant influence on subjective status of private business owners. Meanwhile, we find supportive evidence of Easterlin paradox among Chinese private entrepreneurs: both personal income and operating revenue enhance subjective status in a certain period, but subjective status declines as time goes by, even as incomes continue to rise. A further Oaxaca–Blinder decomposition suggests that the competition and provincial factors are the keys to explaining the paradox. Finally, we try to verify the positive effect of political connection on entrepreneurs' subjective status to provide some helpful advice for the government.

Cycle-skipping problem is one of the major impediments for full-waveform inversion (FWI) to accurately recover subsurface velocity models. The multiscale inversion scheme is a practical and robust strategy to mitigate the cycle-skipping issue. The success of this strategy depends on the existence of low effective frequency components in prestack seismograms. Due to the limited frequency band of the source and receiver, as well as the effects of noises, the low-frequency signals in seismic records are usually either too noisy or totally absent. We explore the possibility of compensating certain low-frequency components for observed band-limited records and apply compensated data to FWI for accurately building subsurface velocity models. We first assume the convolution model holds true for the prestack seismograms, and validate this assumption using a numerical experiment for the Marmousi model. We find that when there are effective low-frequency signals in the records, high-frequency waveforms can be converted to low-frequency waveforms using a deconvolution and convolution filter. However, this strategy fails when the low-frequency signals are missing. Considering the sparsity of seismic wave arrivals, we propose first to estimate Green’s function by solving an $L_{1}$ -norm regularized linear inverse problem, and then construct a new dataset by convolving the estimated Green’s function with a new source function with low-frequency components. Numerical examples for 2-D Amoco and Marmousi models show that the low-frequency compensated data are consistent with the reference data that are computed by using a source wavelet with low-frequency components. By incorporating this low-frequency compensation strategy into a traditional multiscale FWI scheme, we design a modified workflow for recovering the subsurface velocity model hierarchically. Numerical experiments demonstrate that the compensated low-frequency information enables us to resolve large-scale velocity perturbations and mitigate the cycle-skipping problem.

Conventional full-waveform seismic inversion (FWI) tries to estimate a subsurface model that can accurately predict surface records by minimizing an ${L} _{\mathbf {2}}$ -norm misfit between observed and synthetic data. If the initial model is far away from the true model, the cycle-skipping issue might occur and the ${L} _{\mathbf {2}}$ -norm-based FWI produces a spurious model. To mitigate this problem, we present a novel FWI scheme using a high-dimensional local coherence misfit function. A 2-D/3-D window is first used to extract local seismic waveform from the common-shot gathers. Then, we apply a normalized cross-correlation to measure the coherence of local synthetic and observed records, which is used as the misfit to iteratively update the subsurface velocity model. The new misfit function enhances the contribution of phase fitting while reducing the amplitude contribution, which helps to increase the tolerance of FWI to an inaccurate initial velocity model. In addition, the computation of local waveform coherence along the temporal and spatial axes can adaptively balance the adjoint source amplitudes for strong near-offset reflections and weak far-offset refractions, which improves the low- wavenumber updates. Numerical experiments for synthetic and field data demonstrate that the proposed FWI scheme has a better tolerance to inaccurate starting models and is less sensitive to cycle-skipping issues compared with the conventional FWI method.

Compared with the traditional adjoint migration, the least-squares migration (LSM) can effectively mitigate the unbalanced illumination and limited resolution associated with finite acquisition apertures, complex overburden structures, and band-limited records. Data-domain LSM needs many times of Born modeling and adjoint migration to converge to a good solution, which is still challenging for large-scale 3-D models under the current computational capacity. To reduce computational cost and produce high-quality images, we directly approximate the Hessian inverse in the image-domain LSM using a new framework of generative adversarial networks (GANs). The migrated images, source illumination, and migration velocity model are used as input data for the GANs, and the ground-truth reflectivity is utilized as the label data to train the network. Directly applying a conventional GAN framework to implement the image-domain LSM leads to dislocated reflection events and incorrect images. To overcome this issue, we develop a new GAN framework that is more suitable for the Hessian approximation of image-domain LSM, which is named as LsmGANs. In the new framework, we use max-pooling instead of convolution to downsample the feature maps to capture horizontal and vertical variations of reflectors. This enables us to map reflection events to the correct location in downsampling. To address the lateral discontinuity of events in the predicted image from conventional GANs, we further apply multiple transform layers to strengthen feature transformation to guide Hessian approximation. Finally, we add the skip connection in the transform layer to enhance the information exchange of the feature channels and avoid the gradient vanishing problem to improve image resolution. Assembling predicted patches to construct a whole reflectivity image is a key step in the neural network-based LSM. We investigate four strategies using different overlapping ratios and window functions to assemble the LSM patches and observe that less overlapping produces more patch-edge artifacts and the partition of unit with a Gaussian window has the best performance. Numerical experiments for synthetic and field data show that the proposed LsmGAN method can produce high-quality images with balanced amplitudes, reduced artifacts, and improved resolution.

Given the increasing popularity and demand for connected and autonomous vehicles (CAVs), Eco-driving and platooning in highways and urban areas to increase the efficiency of the traffic system is becoming a possibility. This paper presents an Eco-driving trajectory planning approach for a platoon of heterogeneous electric vehicles (EVs) in urban environments. The proposed control strategy for the platoon considers energy consumption, mobility and passenger comfort, with which vehicles may pass signalized intersections with no stops. For a given urban route, first, the platoon's leader vehicle employs dynamic programming (DP) to plan a trajectory for the anticipated path with the aim of balancing energy consumption, mobility and passenger comfort. Then, other following CAVs in the platoon either follow the preceding vehicles, using a PID-based cooperative adaptive cruise control, or plan their own trajectory by checking whether they can pass the next intersection without stopping. Furthermore, a heavy vehicle that cannot efficiently follow a light-weight vehicle would instead employ the DP-based trajectory planner. The results of simulation studies demonstrate the efficacy of the proposed control strategy with which the platoon's energy consumption is shown to reduce while the mobility is not compromised.

Vehicle classification holds significant importance in various domains such as infrastructure design and freight analysis. This study presents an innovative composite deep-learning framework for accurate vehicle classification. The framework exploits two distinct types of features extracted from vehicle images: (1) high-level encodings from state-of-the-art vision transformers (ViTs), and (2) localized vehicle wheel position features obtained through real-time object detection models. The former encapsulates global and semantic characteristics, while the latter focuses on specific wheel (axle) positions. Within this composite model paradigm, we evaluate and compare the efficacy of four ViT models: the original ViT, Cross ViT, Transformer-in-Transformer, and Swin Transformer. Similarly, we assess four object detection models for extracting wheel position features: two Faster R-CNN models (with ResNet-50 and MobileNetv3 backbones) and two YOLO models (YOLOv4 and YOLOR). The ViT encodings and wheel position features are then combined and channeled into a multi-layer perceptron classifier for precise vehicle classification. To enhance the ViT model's effectiveness, we employ a wheel masking strategy during its training, which acts as a regularizer, promoting robust and complementary encodings. Our experimental results reveal that introducing randomness by masking a single wheel significantly enhances the inference performance across all composite models. However, masking more wheels introduces excessive noise and causes performance degradation. Furthermore, initializing ViT encoders with pretrained weights through self-supervised methods leads to additional performance improvements. Notably, our best model achieves an impressive Top-1 classification accuracy of 96.7% when categorizing 13 vehicle classes as defined by the Federal Highway Administration. The results underscore the efficacy of the proposed composite architecture in achieving high precision in vehicle classification tasks.

This research introduces an innovative method for detecting subsurface cracks within pavements by leveraging ground penetrating radar (GPR) technology in conjunction with advanced deep learning techniques. Its primary aim is to significantly improve the accuracy and efficiency of pavement assessment, particularly for operational and maintenance purposes. The proposed model, GPR-YOLOR (You Only Learn One Representation), extends the YOLOR framework and incorporates a region of interest within the top pavement layer to detect subsurface cracks. While the model can be trained with annotated data, the main challenge lies in validating results in the field because of the inability to visually inspect subsurface conditions and the high cost associated with direct coring. To overcome this challenge, we propose an alternative approach that utilizes the co-occurrence of surface cracks as pseudo labels, allowing for easy verification. To ensure that surface cracks correspond to subsurface cracks, the focus is exclusively on transverse cracks that develop in a bottom-up manner, such as fatigue and reflective cracks. Through this methodology, our GPR-YOLOR model achieves an F1 score of 0.72, with a precision of 0.76 and a recall of 0.68. The results from field validation underscore the effectiveness of the GPR-YOLOR model in accurately identifying subsurface cracks, highlighting its practical significance in conducting field condition assessments.

Reclaimed asphalt pavement (RAP) has seen increasing use because it significantly reduces the paving cost, conserves energy, and protects the environment. This paper evaluates how RAP content and binder grade affect the dynamic modulus |E∗| of asphalt concrete mixtures. The mix design includes four RAP contents (0%, 15%, 25% and 30%) and three binder grades (PG64-22, PG67-22, PG76-22). Dynamic modulus tests were performed and |E∗| was measured at four levels of temperatures (40, 70, 100, 130 °F) and six loading frequencies (25, 10, 5, 1, 0.5, 0.1 Hz) in accordance with AASHTO T 342. Based on the test results, a conditional inference tree was estimated, which showed a higher level of RAP content or binder grade generally results in a higher |E∗|. Across the four levels of RAP contents, a significant increase in |E∗| occurred as RAP increased up to 30%. The hierarchical structure of the estimated conditional inference tree reveals the interplay between the two mix design variables (i.e., RAP content and binder grade) depending on the levels of loading frequency and temperature. In general, the binder grade has a stronger association with |E∗| than the RAP content when the reduced frequency is high while the RAP content has a stronger association with |E∗| than the binder grade when the reduced frequency is low. This implies that a balanced mix design should be sought in selecting proper levels of RAP content and binder grade subject to application contexts in terms of temperature and traffic loading.

Wireless magnetometers have been considered as a practical alternative to inductive loops and suitable for large intersections, where span wire is generally used for traffic signal support. In this paper, wireless magnetometers are evaluated for stop bar vehicle detection at signalized intersections. High-resolution detector data were collected in the field subjected to various weather and environmental conditions. Conditional inference trees were used to correlate detection errors with weather and environmental factors that potentially affect the performance of wireless magnetometers. The study results indicated that the wireless magnetometers are fairly robust to various environmental conditions, such as wind, lighting, and visibility. Frequent passing of heavy vehicles, common at large intersections, can cause communication interruption between in-pavement sensors and the access point. This likely increases false and stuck-on call errors, which could be aggravated by adverse weather (e.g., rain, fog, or snow). This communication interruption issue can be mitigated by proper installation of additional repeaters. Provided the interruption issues are site-specific, professional judgment and field test are required for proper system setup, which is critical to delivering accurate and reliable detection for the wireless magnetometer system.

In this paper, carbon dots (CDs) emitting blue fluorescence were prepared using ammonium citrate and ammonium thiocyanate through a low-cost hydrothermal approach. The synthesized carbon dots with maximum excitation and emission wavelengths at 350 nm and 443 nm, respectively, displayed excitation-independent photoluminescence behavior. The relative quantum yield of the as-prepared carbon dots was measured to be 14%. Importantly, a novel method was designed for rifampicin detection as the fluorescence intensity of carbon dots could be quenched significantly by rifampicin on the basis of the inner filter quenching effect. Under optimal experimental conditions, a satisfactory linear response relation was observed between the fluorescence quenching intensity of the carbon dots and the concentration of rifampicin in the range from 0.43 to 49.0 mg L−1; moreover, the detection limit for rifampicin was as low as 0.13 mg L−1. Therefore, a new method has been successfully established, and it is capable of quantification of rifampicin in real samples.

d-Penicillamine (d-PA), as the dominant enantiomer, is usually used in many clinical drugs, but the other enantiomer, l-penicillamine (l-PA) is a toxic enantiomer. Therefore, it is of great significance to analyze and detect l-PA in biomedical or environmental samples. CdTe quantum dots (CdTe QDs) were modified by N-acetyl-l-cysteine (NALC) as novel fluorescent probes and were successfully utilized for the determination of l-penicillamine. The fluorescence of the CdTe QDs decreased significantly in the presence of cupric ions (Cu2+) due to the binding of Cu2+to NALC on the surface of the CdTe QDs, but the addition of l-PA immediately resulted in efficient fluorescence recovery of the modulated CdTe QDs. The experimental results showed that the pH of the buffer solution and the concentration of Cu2+affected the fluorescence intensity upon adding l-PA. Under the optimum conditions, the recovered fluorescence intensity was linearly proportional to the increasing l-PA concentration in the range of 2 × 10−7to 4 × 10−6mol L−1, with correlation coefficient R2= 0.9980. So, a new, simple and rapid method for the determination of l-PA has been developed and its potential has been demonstrated in human serum samples and environmental samples with satisfactory results.

Allura red (AR) is a common food additive. It is of significance to detect AR sensitively and selectively in soft drinks. In this report, fluorescence spectra of allura red-rhodamine dyes systems were studied. In a pH 6.0 Britton–Robinson buffer medium, the fluorescence of rhodamine dyes, such as rhodamine B (RB), butylrhodamine B (BRB) and rhodamine 6G (R6G), can be quenched by AR. Impressively, the emission spectrums of the RB and BRB change slightly upon the addition of AR, but it was clear that the emission of R6G decreased dramatically in the presence of AR. Thus, we have succeeded in planning an improved method for specifically detecting AR on the basis of hydrophobic forces and the electrostatic attraction between R6G and AR. The results show that AR could combine with R6G to form an ionassociation complex, which causes quenching of the emission intensity of R6G and changes of the UV-visible spectra. In this system, 0.097–6.0 μmol L–1AR could be simply detected owing to the decreased fluorescence of R6G in soft drinks, with a detection limit of 0.029 μmol L–1. In addition, we also optimized the reaction conditions and evaluated the effects of some coexisting substances. According to the fluorescence decay time, the UV-visible absorption spectra and the Stern–Volmer plots, the fluorescence quenching of R6G by AR is a static quenching process.

A new methodology is proposed to establish practical guidelines for four incremental left-turn treatments: (a) permissive single left-turn lane, (b) protected–permissive single left-turn lane, (c) protected dual left-turn lanes with equal lane use, and (d) protected dual left-turn lanes with unequal lane use. Decision boundaries were derived from the equilibrium at which the delays of two incremental treatments were equal. The benefits and costs associated with different left-turn treatments—including safety impact and construction and maintenance costs—also were considered. These benefits and costs effectually shift the boundary curves for more realistic decision making.

A new methodology is proposed to establish practical guidelines for four incremental left-turn treatments: (a) permissive single left-turn lane, (b) protected–permissive single left-turn lane, (c) protected dual left-turn lanes with equal lane use, and (d) protected dual left-turn lanes with unequal lane use. Decision boundaries were derived from the equilibrium at which the delays of two incremental treatments were equal. The benefits and costs associated with different left-turn treatments—including safety impact and construction and maintenance costs—also were considered. These benefits and costs effectually shift the boundary curves for more realistic decision making.

Video-imaging technology has long been used for vehicle detection at signalized intersections. This technology provides many advantages over conventional inductive loops. Video-imaging cameras are easier and safer to install and maintain; the cameras cause no damage to the pavement; and detection zones can be adjusted or redrawn when travel lanes are realigned or reassigned as a result of widening or remarking of the pavement. Despite the advantages, video-imaging cameras are generally susceptible to varying lighting conditions. Thermal-imaging cameras produce images on the basis of the difference in thermal energy emitted by objects and do not rely on visible light. Thus, the cameras are more robust in various lighting conditions. In this paper, both video-imaging and thermal-imaging technologies were evaluated for stop bar vehicle detection at signalized intersections. The detection errors were correlated with concurrent weather and environmental conditions. The performance of both types of cameras appears to be affected by wind and various weather events. In particular, uneven shade appears to be an issue for the video-imaging camera but not for the thermal-imaging camera. The video-imaging camera seems to be more susceptible to false calls at night, likely caused by vehicle headlights. A higher chance of missed calls and calls stuck on was experienced for the thermal-imaging camera at night.

A simple, rapid and effective method for auramine O (AO) detection was proposed by fluorescence and UV-Vis absorption spectroscopy. In the BR buffer system (pH 7.0), AO had a strong quenching ability to the fluorescence of bovin serum albumin (BSA) by dynamic quenching. In terms of the thermodynamic parameters calculated as ΔH> 0 and ΔS> 0, the resulting binding of BSA and AO was mainly attributed to the hydrophobic interaction forces. The linearity of this method was in the concentration range from 0.16 to 50 μmol L–1with a detection limit of 0.05 μmol L–1. Based on fluorescence resonance energy transfer (FRET), the distance r(1.36 nm) between donor (BSA) and acceptor (AO) was obtained. Furthermore, the effects of foreign substances and ionic strength were evaluated under the optimum reaction conditions. BSA as a selective probe could be applied to the analysis of AO in medicines with satisfactory results.

Video-imaging technology has long been used for vehicle detection at signalized intersections. This technology provides many advantages over conventional inductive loops. Video-imaging cameras are easier and safer to install and maintain; the cameras cause no damage to the pavement; and detection zones can be adjusted or redrawn when travel lanes are realigned or reassigned as a result of widening or remarking of the pavement. Despite the advantages, video-imaging cameras are generally susceptible to varying lighting conditions. Thermal-imaging cameras produce images on the basis of the difference in thermal energy emitted by objects and do not rely on visible light. Thus, the cameras are more robust in various lighting conditions. In this paper, both video-imaging and thermal-imaging technologies were evaluated for stop bar vehicle detection at signalized intersections. The detection errors were correlated with concurrent weather and environmental conditions. The performance of both types of cameras appears to be affected by wind and various weather events. In particular, uneven shade appears to be an issue for the video-imaging camera but not for the thermal-imaging camera. The video-imaging camera seems to be more susceptible to false calls at night, likely caused by vehicle headlights. A higher chance of missed calls and calls stuck on was experienced for the thermal-imaging camera at night.

This study examined the effect of the materials and age on the coefficient of thermal expansion (CTE) of concrete paving mixtures at 28 and 120 days. Concrete specimens were prepared in the laboratory by varying the mix design variables with different types of aggregate to produce different mixes using Portland cement. The microstructures of the concrete mixtures were also observed by scanning electron microscopy to determine the relationship between the volume change in concrete and the formation of microcracks. Statistical analyses of the experimental data suggested that the CTEs measured at 120 days are significantly lower than those measured at 28 days. An analysis of variance indicated that the mixture with granite results in significantly higher CTE reduction with time than the mixture with dolomite. The larger volume change with a higher CTE may be due to the formation of microcracks in concrete.

The dienol of ascorbic acid was observed to have a strong reducing power in a 1.44 M HCl buffer solution, and could reduce Fe3+to Fe2+. The resulting Fe2+was allowed to react with K3Fe(CN)6to form the ion-association complex Turnbull's blue, which resulted in the enhancement of resonance Rayleigh scattering (RRS), second order scattering (SOS) and frequency doubling scattering (FDS). The maximum scattering intensities occurred at wavelengths of 374 nm, 540 nm and 395 nm, respectively. The scattering intensities of the three methods were proportional to the concentration of ascorbic acid in a certain range. RRS was found to be the most sensitive method here, and its detection limit for ascorbic acid was 5.0 ng mL−1. The experimental conditions were optimized and the effects of coexisting substances were evaluated. The method showed excellent selectivity, because relatively high amounts of common ions, acid groups, urea, amino acid, saccharides and protein were found not to influence the measurement. Accordingly, a novel, rapid, convenient, sensitive and selective RRS method for determination of ascorbic acid concentration has been proposed, and applied to detect ascorbic acid in tablets and human serum samples with satisfactory results. The reaction mechanism and causes of the resonance light scattering intensity enhancement are also discussed.

A novel simple sensitive and inexpensive method based on the formation of Turnbull's blue nanoparticles using resonance Rayleigh scattering has been developed for the determination of hydroquinone (HQ). The results show that Fe3+is deoxidized to Fe2+by hydroquinone in pH 2.7 HCl (0.002 mol L−1) solution, then Fe2+reacts with potassium ferricyanide to form a soluble Turnbull's blue (KFe[Fe(CN)6]), and further aggregates to form {KFe[Fe(CN)6]}nnanoparticles. These results induce a significant enhancement of resonance Rayleigh scattering (RRS). The maximum scattering wavelength of the ion-association complex is located at about 310 nm. The increment of scattering intensity (IRRS) is directly proportional to the concentration of HQ in the range of 0.46–50.0 μmol L−1. This method has high sensitivity and the detection limit (3σ/k) for HQ is 0.14 μmol L−1. In this work, the characteristics of absorption and RRS spectra of this reaction have been studied. The optimum reaction conditions and influencing factors have been investigated. Furthermore, the reaction mechanism and the reasons for the RRS enhancement have been explored. Additionally, the method is applied to the determination of HQ in local river water samples with satisfactory results.

By fitting observed data with predicted seismograms, least‐squares migration (LSM) computes a generalized inverse for a subsurface reflectivity model, which can improve image resolution and reduce artifacts caused by incomplete acquisition. However, the large computational cost of LSM required for simulations and migrations limits its wide applications for large‐scale imaging problems. Using point‐spread function (PSF) deconvolution, we present an efficient and stable high‐resolution imaging method. The PSFs are first computed on a coarse grid using local ray‐based Gaussian beam Born modeling and migration. Then, we interpolate the PSFs onto a fine‐image grid and apply a high‐dimensional Gaussian function to attenuate artifacts far away from the PSF centers. With 2D/3D partition of unity, we decompose the traditional adjoint migration results into local images with the same window size as the PSFs. Then, these local images are deconvolved by the PSFs in the wavenumber domain to reduce the effects of the band‐limited source function and compensate for irregular subsurface illumination. The final assembled image is obtained by applying the inverse of the partitions for the deconvolved local images. Numerical examples for both synthetic and field data demonstrate that the proposed PSF deconvolution can significantly improve image resolution and amplitudes for deep structures, while not being sensitive to velocity errors as the data‐domain LSM. Seismic imaging is an important tool to detect hydrocarbon resource and study deep Earth's structure. Traditional ray‐based and wave equation imaging methods commonly extrapolate observed data and apply appropriate imaging condition to construct subsurface impedance interfaces. Mathematically, these methods can be considered as adjoint operators of seismic modeling, but they have difficulty to produce high‐quality images of complicated subsurface structures. To mitigate this issue, we present an efficient and stable high‐resolution imaging approach, i.e., the point‐spread function (PSF) deconvolution. The locally temporal and spatial limitations are utilized to efficiently compute the PSFs on a coarse grid using a ray‐based Gaussian beam propagator. Then, an on‐the‐fly interpolation on a fine grid and a wavenumber‐domain deconvolution are applied to improve image resolution. Numerical experiments for benchmark model and field data demonstrate the feasibility and adaptability of the proposed method for imaging complicated salt structure and low‐signal‐to‐noise data. Conventional least‐squares migration is too expensive to be widely applied for large‐scale problems under current computational capacityAn efficient approach for calculating point‐spread functions using local modeling and migration is proposedThe point‐spread function deconvolution is then applied to incorporate the Hessian effect and improve image quality Conventional least‐squares migration is too expensive to be widely applied for large‐scale problems under current computational capacity An efficient approach for calculating point‐spread functions using local modeling and migration is proposed The point‐spread function deconvolution is then applied to incorporate the Hessian effect and improve image quality

Ray theory gives a high‐frequency asymptotic solution of the wave equation, which has been widely used in the seismic study because of its high computational efficiency and good physical insights for elementary waves. However, the fundamental high‐frequency assumption makes it difficult to accurately simulate the finite‐frequency effect of wave propagations. On the other hand, the Gaussian beam, as one of the advanced ray‐based methods, overcomes the amplitude singularity problem near the caustics by introducing a complex‐valued paraxial approximation. But Gaussian beams only use the velocity and up to second‐order velocity derivative of central rays and thus does not accurately simulate the response of heterogeneity away from the rays. To bridge the gap between the ray theory and wave‐equation methods, we present a two‐way beam wave method and apply it to seismic imaging. A fan of central rays are first shot to extract local ray‐centered model parameters in the global Cartesian coordinates. Then, a finite‐difference method is used to solve the acoustic wave equation in the ray‐centered coordinates to simulate wave propagations near the central rays. The beam wave method can accurately simulate the response of the velocity heterogeneities in the ray tubes and therefore produces more accurate wavefields. In addition, we discretize the ray‐centered wave equation onto a non‐orthogonal grid, which considerably reduces the computational cost. We apply the two‐way beam wave method in seismic imaging and develop a beam wave reverse‐time migration. It inherits the advantages of both ray‐based and wave equation migrations, and produces clear images for complicated structures with fewer artifacts than traditional imaging methods. Seismic ray theory has evolved from classical asymptotic ray theory, through paraxial ray theory and Maslov's method, and then to the Gaussian beam method. Although the computational accuracy of the ray‐based methods for seismic modeling and imaging have been greatly improved, the fundamental high‐frequency approximation still prevents them from accurately describing finite‐frequency wave propagations in complex structures. We introduce the idea of reverse‐time migration into the beam method and develop a two‐way beam wave scheme for seismic modeling and imaging. Local velocities in the vicinity of central rays are first extracted and then the finite‐difference approach is used to solve the wave equation in the ray‐centered coordinate. Accurate wavefield simulation and beam‐to‐beam imaging condition enables us to achieve clear migrated images with fewer artifacts than traditional Gaussian beam migration and reverse‐time migration. Numerical examples for benchmark models and field data demonstrate the feasibility and adaptability of the proposed two‐way beam wave method. It is difficult for ray theory to accurately simulate seismic wave phenomena in areas with strong velocity variationsA two‐way beam wave method is proposed to bridge the gap between ray‐based and wave equation approachesA beam wave reverse‐time migration approach is developed to improve the image quality for complicated subsurface environments It is difficult for ray theory to accurately simulate seismic wave phenomena in areas with strong velocity variations A two‐way beam wave method is proposed to bridge the gap between ray‐based and wave equation approaches A beam wave reverse‐time migration approach is developed to improve the image quality for complicated subsurface environments

The coefficient of thermal expansion (CTE) is one of the most critical parameters for concrete pavement design. Concrete paving mixtures with high CTEs are generally subjected to temperature-related stresses and deterioration. In this study, a 2kdesign of experiment was applied to evaluate the effects of concrete mix design variables on the CTE of resultant concrete mixtures. Laboratory experiments were conducted by controlling six mix design variables, each at two levels, including aggregate type (granite or dolomite), fine sand type (manufactured or natural), fly ash type (Class C or Class F), cement content (314 or 273 kg/m 3), coarse aggregate content (1246 or 682 kg/m 3), and air content (3% or 6%). The respective and collective effects of these factors on the CTE were analysed. It showed that the type of coarse aggregate, fine sand, fly ash, and their respective contents are significant in explaining the variance of the lab-measured CTEs. The minimum CTE is a result of the concrete mixture consisting of low content of granite and cement in combination with high content of manufactured sand and Class F fly ash. To generalise the results, a parsimonious model was formulated and is capable of explaining over 92% of the total variance of the CTE and predicting it in a reasonably accurate manner.

A sensitive and simple method for the determination of fleroxacin (FLO) was developed based on the changes of dual-wavelength fluorescence signals of glutathione (GSH)-capped CdTe quantum dots (QDs). Under optimum conditions, a linear calibration plot of the quenched fluorescence intensity at 555 nm against the concentration of FLO was observed in the range of 0.0262–35.0 μg mL−1with a correlation coefficient (R) of 0.9992. Meanwhile, a linear calibration plot of the enhanced fluorescence intensity at 429 nm against the concentration of FLO was observed in the range of 0.1966–35.0 μg mL−1with a correlation coefficient (R) of 0.9986. The corresponding detection limits (3σ/K) are 17.93 ng mL−1at 555 nm and 59.58 ng mL−1at 429 nm. In addition, a detailed reaction mechanism has also been proposed on the basis of electron transfer supported by ultraviolet-visible (UV-vis) absorption, fluorescence (FL) spectroscopy and the measurement of fluorescence lifetime. The method has been applied for the determination of FLO in pharmaceutical preparations with satisfactory results. The proposed method manifested several advantages such as high sensitivity, short analysis time, low cost and ease of operation.

In this work, the fluorescence of a metal ion (UO22+) was exploited for the determination of meloxicam. The fluorescence quenching values of uranyl acetate (UA) showed an excellent linear relationship with the concentration of meloxicam (MLX) over the range of 0.015–7.5 μg mL−1. The detection limit (3σ) was 4.49 ng mL−1, which is lower than or comparable to most of the previously reported studies. The proposed method was applied satisfactorily to the assay of MLX in tablet and capsule samples. The optimum reaction conditions, influencing factors and effects of coexisting substances were investigated. In addition, the absorption spectrum, quenching constants (Kq) and Stern–Volmer plots confirmed that this was a dynamic-quenching process.

With the growing need to maintain roadway systems despite increasing competition for resources while ensuring safety and comfort for travelers, sound network-level decision making becomes more vital than ever. A stochastic process known as the Markov chain has been used extensively to capture the uncertainty associated with pavement performance over time and to support this critical decision-making process. By application of the Markov chain, this paper investigates the crack histories of flexible pavements to gain insight into the impacts of two primary factors that contribute to the rapid deterioration of surface cracks in flexible pavements: excessive traffic loading and delayed maintenance and rehabilitation. The empirical results of the investigation, obtained by using the data from the Florida Department of Transportation's pavement condition survey database, are presented. The results show that the impacts of the two factors mentioned above are statistically different from one another in terms of the rate of deterioration of Florida's pavements because of cracks. These findings will assist highway authorities in making more timely and efficient network-level decisions.

The pavement Mechanistic-Empirical (ME) design requires high-dimensional traffic feature inputs by categories, including Vehicle Class Distributions (VCD), Monthly Distribution Factors (MDF), Hourly Distribution Factors (HDF), and Normalized Axles Load Spectra (NALS). In simplifying the Pavement ME design practice, Truck Traffic Classification (TTC) groups are commonly used for characterizing traffic inputs. Thus, properly defining TTC groups is critical for state-specific pavement ME design practice. In this study, the truck traffic data from existing Weight-in-Motion (WIM) stations were mined to develop specific TTC groups to assist with pavement ME design practice in Georgia. An effective data analytics procedure was developed by leveraging unsupervised machine learning techniques to reduce the high-dimensional traffic features by stratified Principal Component Analysis (PCA), followed by K-means clustering to establish appropriate TTC groups. For a case study, the performance of two typical designs was evaluated using the AASHTOWare pavement mechanistic-empirical (ME) design software with respect to two scenarios of traffic inputs: (1) the derived cluster-based groups, and (2) the national default TTC groups. The results indicated that direct application of the national default TTC groups resulted in over-design of pavement structure in Georgia. Therefore, it is highly recommended that customized TTC groups should be developed using state-specific WIM data.

Pavement cracking and rutting are two of the most critical distress types manifested on flexible pavements, and they often govern the overall pavement condition. Hence, many models have been developed for forecasting the deterioration of the crack condition accurately, with the traditionally preferred technique being the use of a regression relationship developed from laboratory or field statistical data, or both. However, it becomes tedious for regression techniques to predict crack performance accurately and robustly in the presence of the multitude of tributary factors, material nonlinearity, and uncertainty involved in the cracking process. With the advancement of modeling techniques, two innovative breeds of models, neural networks and recurrent Markov chains, have drawn increasing attention from researchers for their use in modeling complex phenomena such as pavement cracking. This paper compares the ability of neural networks and recurrent Markov chains to model crack performance, using the Florida Department of Transportation's pavement condition data. Comparable forecasting accuracies have been indicated for single-year forecasts using both models. When the models were applied for multiple-year prediction of crack performance, the recurrent Markov chain tends to produce more consistent forecasts as compared to the neural network, which exhibits a tendency toward overpredicting crack deterioration. Finally, further discussion gives an insight into the structure of each model.

Pavement cracking and rutting are two of the most critical distress types manifested on flexible pavements, and they often govern the overall pavement condition. Hence, many models have been developed for forecasting the deterioration of the crack condition accurately, with the traditionally preferred technique being the use of a regression relationship developed from laboratory or field statistical data, or both. However, it becomes tedious for regression techniques to predict crack performance accurately and robustly in the presence of the multitude of tributary factors, material nonlinearity, and uncertainty involved in the cracking process. With the advancement of modeling techniques, two innovative breeds of models, neural networks and recurrent Markov chains, have drawn increasing attention from researchers for their use in modeling complex phenomena such as pavement cracking. This paper compares the ability of neural networks and recurrent Markov chains to model crack performance, using the Florida Department of Transportation's pavement condition data. Comparable forecasting accuracies have been indicated for single-year forecasts using both models. When the models were applied for multiple-year prediction of crack performance, the recurrent Markov chain tends to produce more consistent forecasts as compared to the neural network, which exhibits a tendency toward overpredicting crack deterioration. Finally, further discussion gives an insight into the structure of each model.

Timely identification of undesirable crack, ride, and rut conditions is a critical issue in pavement management systems at the network level. The overall pavement surface condition is determined by these individual pavement surface conditions. A research project was carried out to implement an overall methodology for pavement condition prediction that uses artificial neural networks (ANNs). In the research, three ANN models were developed to predict the three key indices—crack rating, ride rating, and rut rating—used by the Florida Department of Transportation (FDOT) for pavement evaluation. The ANN models for each index were trained and tested by using the FDOT pavement condition database. In addition to the three key indices, FDOT uses a composite index called pavement condition rating (PCR), which is the minimum of the three key indices, to summarize overall pavement surface condition for pavement management. PCR is forecast with a combination of the three ANN models. Results of the research suggest that the ANN models are more accurate than the traditional regression models. These ANN models can be expected to have a significant effect on FDOT's pavement management system.

Timely identification of undesirable crack, ride, and rut conditions is a critical issue in pavement management systems at the network level. The overall pavement surface condition is determined by these individual pavement surface conditions. A research project was carried out to implement an overall methodology for pavement condition prediction that uses artificial neural networks (ANNs). In the research, three ANN models were developed to predict the three key indices—crack rating, ride rating, and rut rating—used by the Florida Department of Transportation (FDOT) for pavement evaluation. The ANN models for each index were trained and tested by using the FDOT pavement condition database. In addition to the three key indices, FDOT uses a composite index called pavement condition rating (PCR), which is the minimum of the three key indices, to summarize overall pavement surface condition for pavement management. PCR is forecast with a combination of the three ANN models. Results of the research suggest that the ANN models are more accurate than the traditional regression models. These ANN models can be expected to have a significant effect on FDOT's pavement management system.

Highway safety is largely influenced by weather conditions that have become increasingly volatile due to the climate change. It well known that wet pavement significantly reduces surface friction, leading to inflated collision risk. Thus, timely knowledge of the road surface condition is critical for safe driving. In this paper, a novel machine learning model pipeline is proposed to detect the wetness of pavement based on live images of highway scenes captured by publicly accessible traffic cameras. To simplify the learning task, we finetuned the state-of-the-art instance segmentation baseline models to extract background instance targets, including pavement, sky, and vegetation, which are common in highway scenes. Then, the color mixture attributes in HSV (hue, saturation and value) of each segmented instance were extracted and used as visual cues for inferring pavement condition. Finally, gradient boosting ensemble classifiers are constructed and trained using the HSV features to predict the wetness of pavement. For the segmentation task, we leveraged Detectron2 baseline models (Mask R-CNN) and evaluated three backbone networks: R50-FPN, R101-FPN, and X101-FPN. For the classification task, two most popular gradient boosting algorithms (XGBoost and CatBoost) were evaluated together with a classic logistic model. Based on experiments with our custom dataset, the best performance (F1score: 0.927, AUC: 0.975) was achieved by the R101-FPN backbone coupled with the CatBoost classifier.

Studies have been performed in spray cooling with phase change using water as the coolant. A gas atomizing nozzle was used with both air and steam as the driving gases. The effect of gas atomizing pressure and liquid flow rate on the heat transfer, specifically, the critical heat flux, is studied. Spray droplet size and velocity, liquid film thickness, and flatness were measured using phase Doppler, Fresnel diffraction, and holographic techniques, respectively. The effect of spray characteristics on film thickness and heat transfer is discussed. The studies indicate that for a given secondary gas flow rate there exists an optimal liquid flow rate at which a maximum CHF is obtained. The spray cooling technique provides a wide heat flux operating range for small changes in system temperatures.

On March 31, 2020, an Mw6.5 earthquake struck near Stanley, Idaho. More than 35% nondouble‐couple component in long‐period point‐source solutions indicate a more complex source than slip on a planar fault. Using an integrative analysis of seismological and geodetic data, we find that the Stanley earthquake ruptured a pair of opposing‐dip faults offset by a 10‐km‐wide step, including an unmapped northern subfault with predominantly strike‐slip faulting and a southern subfault subparallel to the Sawtooth fault with predominantly normal faulting. This converging fault geometry allowed the rupture to traverse a surficial 10‐km‐wide step, which is greater than the limiting dimension (3–4 km) that commonly ceases earthquake ruptures. This study reveals that a composite rupture process with strike‐slip and normal faulting is typical for earthquakes located near the northern boundary of the Centennial Tectonic Belt (CTB), which is distinct from the predominantly normal faulting in the central CTB. The northwest of Stanley, Idaho, is struck by an Mw6.5 earthquake on March 31, 2020, which is the largest event in Idaho since the 1983 Borah Peak earthquake. This event has several intriguing aspects: (1) the epicenter located by USGS is not on a mapped fault and the source region has little historical seismicity over the past 50 years; (2) it predominantly involves strike‐slip faulting, which is inconsistent with the extensional strain accommodated by the closest Sawtooth fault; (3) long‐period point‐source solutions have more than 35% non‐double‐couple components, indicating a more complex source than slips on a planar fault. Based upon an integrative analysis for both seismological and remote sensing data, we prescribe an opposing‐dip two‐fault model to reconcile all observations. The rupture initiated near the USGS epicenter, and then propagated to the southeast about 20 km along the northerner subfault trajectory. When reaching the northwestern terminus of the Sawtooth fault, the rupture changed its original trajectory and moved southwestward, traversing a 10‐km‐wide step‐over. After passing the mapped Sawtooth fault terminus, it propagated to the southeast about 25 km along the southern subfault, which is subparallel to the Sawtooth fault scarp. An integrative analysis is applied for the seismological and geodetic data of the 2020 Stanley, Idaho earthquakeWe found that the Stanley earthquake ruptured a pair of opposing‐dip faults offset by a 10‐km‐wide step that narrows with depthThis study reveals that complicated rupture processes appear to be typical for the earthquakes located near the northern Centennial Tectonic Belt boundary An integrative analysis is applied for the seismological and geodetic data of the 2020 Stanley, Idaho earthquake We found that the Stanley earthquake ruptured a pair of opposing‐dip faults offset by a 10‐km‐wide step that narrows with depth This study reveals that complicated rupture processes appear to be typical for the earthquakes located near the northern Centennial Tectonic Belt boundary

Several hypotheses have been proposed to explain intriguing circular shear wave splitting patterns in the Pacific Northwest, invoking either 2‐D entrained flows or 3‐D return flows. Here, we present some hitherto unidentified, depth‐dependent anisotropic signatures to reconcile different conceptual models. At depths shallower than 200 km, the fast propagation directions of seismic waves to the west of the Rocky Mountain are aligned sub‐parallel to the subduction direction of the Juan de Fuca and Gorda Plates. This pattern is consistent with previous onshore/offshore shear wave splitting measurements and indicates that 2‐D entrained flows dominate at shallower depths. From 300 to 500 km, two large‐scale return flows are revealed, one circulating around Nevada and Colorado and the other running around the edge of the descending Juan de Fuca slab. These observations suggest the development of toroidal‐mode mantle flows, driven by the fast rollback of the narrow, fragmented Juan de Fuca and Gorda slabs. The fast directions are aligned sub‐parallel to the subduction direction of the Juan de Fuca Plates at depths shallower than 200 kmTwo large‐scale return flows are revealed, circulating around the edges of the descending Juan de Fuca and Gorda slabsThe uppermost lower mantle beneath the Cascadian Subduction Zone is dominated by a NE‐SW oriented flow field

To complement velocity distributions, seismic attenuation provides additional important information on fluid properties of hydrocarbon reservoirs in exploration seismology, as well as temperature distributions, partial melting, and water content within the crust and mantle in earthquake seismology. Full waveform inversion (FWI), as one of the state‐of‐the‐art seismic imaging techniques, can produce high‐resolution constraints for subsurface (an)elastic parameters by minimizing the difference between observed and predicted seismograms. Traditional waveform inversion for attenuation is commonly based on the standard‐linear‐solid (SLS) wave equation, in which case the quality factor (Q) has to be converted to stress and strain relaxation times. When using multiple attenuation mechanisms in the SLS method, it is difficult to directly estimate these relaxation time parameters. Based on a time domain complex‐valued viscoacoustic wave equation, we present an FWI framework for simultaneously estimating subsurface Pwave velocity and attenuation distributions. Because Qis explicitly incorporated into the viscoacoustic wave equation, we directly derive Pwave velocity and Qsensitivity kernels using the adjoint‐state method and simultaneously estimate their subsurface distributions. By analyzing the Gauss‐Newton Hessian, we observe strong interparameter crosstalk, especially the leakage from velocity to Q. We approximate the Hessian inverse using a preconditioned L‐BFGS method in viscoacoustic FWI, which enables us to successfully reduce interparameter crosstalk and produce accurate velocity and attenuation models. Numerical examples demonstrate the feasibility and robustness of the proposed method for simultaneously mapping complex velocity and Qdistributions in the subsurface. Subsurface velocity and attenuation structures are important for studying hydrocarbon reservoirs, geothermal dynamics, and magma chambersAn inversion scheme is developed for simultaneously mapping Pwave velocity and attenuation based on a complex‐valued wave equationAn approximated Gauss‐Newton Hessian is utilized to reduce interparameter crosstalks and produce accurate velocity and attenuation models

Deploying sparse sources and receivers in a complex near‐surface environment for seismic imaging and monitoring remains a challenge. Complicated near‐surface structures with strong lateral velocity variations and thick weathering layers distort seismic wave paths and produce strong reverberations and scattering. Consequently, acquisition with a buried receiver system below the weathering layer is preferred for land surveys, but the cost of this approach is excessively high in real applications. To mitigate this problem, we propose a source‐receiver interferometry‐based redatuming method that can generate redatumed data beneath the near surface with a reduced cost. The new acquisition geometry consists of dense surface and sparse buried receivers. The workflow based on source‐receiver redatuming combines surface and buried systems by cross correlating direct waves recorded by buried receivers with the corresponding reflected waves recorded by surface receivers. The approach transforms surface source‐receiver records into virtually buried source‐receiver records. We effectively suppress the acquisition noises associated with the near‐surface structures because the proposed technique generates redatumed data below the weathering layer. We apply dip‐guided interpolation to the redatumed data to make the geometry consistent with the original surface geometry. The resulting records not only improve the seismic image quality and repeatability compared with the original records but also have a lower cost than a denseburied acquisition system. We use 13 seismic surveys carried out at different times to demonstrate the feasibility and advantage of the proposed method. It is a challenge to use sparse sources and receivers deployed in a complex near‐surface environment for seismic imaging and monitoringIt combines surface and buried system by cross correlating direct waves by buried receivers with the reflected waves by surface receiversThe proposed methods considerably improve seismic repeatability and image quality as demonstrated by 13 seismic surveys

Using regional seismic data and a 3-D crustal velocity model, we develop a novel multiazimuth backprojection approach and apply it to image time-lapse coseismic ruptures for the 2019 Ridgecrest earthquakes. The time-integrated images for the Mw6.4 foreshock and Mw7.1 mainshock agree with the fault geometry delineated by the aftershock distributions. Backprojection images at different times illustrate the detailed rupture processes for these two events. For instance, the Mw6.4 foreshock initialized close to the hypocenter, then the rupture propagated along a northwest trending fault with an average velocity of 1.0 km/s, and finally jumped to a southwest trending fault and propagated about 20 km with a velocity about 1.5 km/s. In contrast, the Mw7.1 mainshock initialized near the hypocenter, then propagated to the northwest upon reaching the Coso volcanic field with an average velocity of 1.4 km/s, and later turned to the southeast and propagated along the main fault zone with a complex bilateral process and a velocity about 0.6 km/s. We present a novel backprojection approach to image time-lapse coseismic rupture propagation for the 2019 Ridgecrest earthquake sequenceThe backprojection images agree with the fault geometry determined by the distribution of aftershocksThe time-lapse images illustrate the detailed rupture processes of the Mw6.4 foreshock and Mw7.1 mainshock

A new azimuthal anisotropy model for the North American and Caribbean Plates, namely, US32, is constructed based on full waveform inversion and records from the USArray and other temporary/permanent networks deployed in the study region. A total of 180 earthquakes and 4,516 seismographic stations are employed in the inversion to simultaneously constrain radially and azimuthally anisotropic model parameters: L, N, Gc, and Gs, within the crust and mantle. Thirty‐two preconditioned conjugate gradient iterations have been utilized to minimize frequency‐dependent phase discrepancies between observed and predicted seismograms for three‐component short‐period (15–40 s) body waves and long‐period (25–100 s) surface waves. Model US32exhibits complicated variations in anisotropic fabrics underneath the western and eastern United States, especially at depths shallower than 100 km. For instance, the fast axis orientations in model US32suggest the presence of trench‐perpendicular mantle flows underneath the Cascadia Subduction Zone and also follow the strikes of the Snake River Plain, the Ouachita Orogenic Front, and the Grenville and Appalachian Orogenic Belts. The amplitudes of azimuthal anisotropy reduce to around 1% at depths greater than 200 km, and the orientations are subparallel to the global plate motion directions to the east of the Rocky Mountain, except for large discrepancies in central and eastern Canada. At a depth of 700 km, the fast axes change along the trajectory of the Farallon slab underneath the Great Lakes region and Gulf of Mexico, which might indicate the development of 2‐D poloidal‐mode mantle flows perpendicular to the strike of the sinking slab within the uppermost lower mantle. Comparisons between model US32with a western U.S. model from ambient noise tomography and SKS splitting measurements demonstrate a relatively good agreement for the fast axis orientations, considering the usage of different data sets and imaging techniques. However, the absolute magnitude of azimuthal anisotropy in model US32might be underestimated, especially at greater depths, given the poor agreement on the amplitudes of predicted and observed SKS splitting times. At the current stage, the agreement among different azimuthal anisotropy models at global and continental scales is still poor even for the United States with a dense station coverage. A new azimuthal anisotropy model US32is developed for North America and the CaribbeanAt depths shallower than 100 km, model US32exhibits short‐wavelength anisotropic fabrics, which correlate with local geological provincesAt depths greater than 200 km, the fast axes in model US32mainly reflect long‐wavelength global plate motion directions