Detection Depth Research Articles

BEVDepth: Acquisition of Reliable Depth for Multi-View 3D Object Detection

In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work is based on a key observation -- depth estimation in recent approaches is surprisingly inadequate given the fact that depth is essential to camera 3D detection. Our BEVDepth resolves this by leveraging explicit depth supervision. A camera-awareness depth estimation module is also introduced to facilitate the depth predicting capability. Besides, we design a novel Depth Refinement Module to counter the side effects carried by imprecise feature unprojection. Aided by customized Efficient Voxel Pooling and multi-frame mechanism, BEVDepth achieves the new state-of-the-art 60.9% NDS on the challenging nuScenes test set while maintaining high efficiency. For the first time, the NDS score of a camera model reaches 60%. Codes have been released.

Protein@cyanine-based NIR-II lymphography enables the supersensitive visualization of lymphedema and tumor lymphatic metastasis.

Visualization of the lymphatic system is clinically indispensable for the diagnosis and/or treatment of lymphatic diseases. Although indocyanine green (ICG) lymphography becomes an alternate imaging modality compared to traditional lymphoscintigraphy, it's still far from ideal due to the insufficient detection depth and low spatiotemporal resolution. Herein, w e rationally develop protein@cyanine probes to solve the limitations of the current NIR-I lymphography. The protein@cyanine probes are synthesized following a chlorine-containing dye-labeling strategy based on structure-selectivity (facile covalent binding between the dye and protein with a 1:1 molar ratio). As expected, ou r probes display exceptional NIR-II imaging ability with much-improved imaging contrast/resolution and controllable pharmacokinetics, superior to the clinical ICG. Ou r protein@cyanine probes locate lymph nodes (LNs) and delineate lymphatic vessels (LVs) with super-high sensitivity and signal-to-background ratio, enabling real-time diagnosing lymphatic diseases such as lymphedema and tumor lymphatic metastasis. In particular, the NIR-II lymphography provides us an opportunity to discover the disparate morbidity rate of primary lymphedema in different types of mice. Given the fact of lacking clinically transferable NIR-II probes, this work not only provides a promising strategy for enriching of the current library of NIR-II probes, but also promotes the clinical translation of NIR-II lymphography technology. This article is protected by copyright. All rights reserved.

Comparison of short-offset and long-offset grounded-wire transient electromagnetic responses based on the 3D model

In this study, a recently developed time-domain electromagnetic method called the short-offset grounded-wire transient electromagnetic (SOTEM) method, which is a near-source observation method, was adopted to obtain strong signals and great detection depths. The responses of the SOTEM and long-offset transient electromagnetic (LOTEM) methods were compared to further guide and promote the SOTEM method. Currently, the comparison between SOTEM and LOTEM methods is primarily based on one-dimensional (1D) models. However, most geological bodies are three-dimensional (3D) structures. We investigated the responses of a grounded-wire transient electromagnetic method based on 3D models using the 3D finite-difference time-domain method. In addition, the signal strengths, detection sensitivities and detection depths of the SOTEM and LOTEM methods were compared. The results revealed that the field amplitudes of Ex (electrical component parallel to the transmitting source) and ∂By/∂t (magnetic component perpendicular to the transmitting source horizontally) were higher at the short offsets than at the long offsets. For the ∂Bx/∂t (magnetic component parallel to the transmitting source) and the vertical magnetic component ∂Bz/∂t, at initial times, the responses received would be stronger when closer to the transmitting source, whereas at later times, the responses would be stronger when farther from the source. Ex detection sensitivity increased with an increase in the offset at initial times, and increased with a decrease in the offset at later times. The detection sensitivities of the three magnetic field components at short offsets were higher than those at long offsets. The ∂By/∂t effective detection depth was the greatest. Generally, the effective detection depths of the three magnetic field components increased with decreasing offset. The range of the ratio of the horizontal distance, r, between the transmitting source and the target body to the effective detection depth H was 0.5-1.1.

A Prediction Model Based on the Long Electrode Source for Fault Anomaly in Tunnel

The resistivity method has been widely used to predict the water-bearing structure of tunnels. The traditional resistivity uses the point electrode (PE) source in the tunnel to excite the electric field. Because the tunnel face is inaccessible, its exploration depth is limited and small. In order to overcome this problem, the horizontal pilot hole is used as the long electrode (LE) source in the tunnel. We use the finite element method (FEM) to establish a three-dimensional modeling algorithm for tunnel detection using a long electrode source. The accuracy of the algorithm is verified by using the long electrode source model. By a lot of numerical simulations, a prediction model of a long electrode source for tunnel detection is firstly proposed. The predicted results show that it has good applicability in detecting long-distance anomaly. The comparison of the long electrode source and point electrode source models shows that the detection depth of the long electrode prediction model is farther than that of the point electrode source. This long electrode source method can improve the construction efficiency and effectively prevent water inrush in the tunnel.

Pumping Induced Hydraulic Gradient Driven Tracer Migration through Defects: Implications for Rapid Detection of Leakage in Vertical Flexible Barrier

Vertical flexible barriers (VFBs) are widely used for contaminant source separation and risk control. Inherent or externally caused damage to a VFB can affect its effectiveness in pollution containment and control, but its effective detection is a major challenge due to its concealed nature. This paper presents a combined pumping and tracer approach to detect defects in VFBs. A high-performance model is used to model the detection process revealing the migration and distribution behavior of the tracer in the triple media system of wellbore–aquifer–VFB to better understand the effectiveness of the method and its performance indicators, including response time, detectable depth, and defect size, and to optimize its application. Simulation results show that the tracer quickly moves through the defect to the monitoring well, and temporal tracer concentration reveals details of the breach in the wall. Sensitivity analysis reveals influential factors, including defect size, depth, and tracer concentration. Results indicate that the size and depth of defects will affect the penetration and response time. Breakthrough is less clear, and response time is longer if defects are small and deep. However, a higher tracer injection concentration can shorten the response time and increase tracer concentration in the monitoring well. Therefore, it is necessary to design the tracer dosing concentration according to the site conditions. For the Yancheng site, tracer concentration was increased from 1000 ppb to 2660 ppb to ensure the detection of leakage points at 4 m or deeper.

Monocular Based Navigation System for Autonomous Ground Robots Using Multiple Deep Learning Models

In recent years, the development of ground robots with human-like perception capabilities has led to the use of multiple sensors, including cameras, lidars, and radars, along with deep learning techniques for detecting and recognizing objects and estimating distances. This paper proposes a computer vision-based navigation system that integrates object detection, segmentation, and monocular depth estimation using deep neural networks to identify predefined target objects and navigate towards them with a single monocular camera as a sensor. Our experiments include different sensitivity analyses to evaluate the impact of monocular cues on distance estimation. We show that this system can provide a ground robot with the perception capabilities needed for autonomous navigation in unknown indoor environments without the need for prior mapping or external positioning systems. This technique provides an efficient and cost-effective means of navigation, overcoming the limitations of other navigation techniques such as GPS-based and SLAM-based navigation.Graphical

Design and Implementation of Three-Channel Drainage Pipeline Ground Penetrating Radar Device.

In order to solve the current problems that conventional video inspection can only detect, as an internal pipeline defect and drainage pipeline radar inspection device detects in a single direction and at radar frequency in water pipeline defect detection, a three-channel drainage pipeline ground penetrating radar (GPR) inspection device was designed and developed, the assembly and commissioning of the device prototype were completed, and an actual engineering test application was carried out. Focusing on the problem that the detection direction and depth of the single-channel detection device are limited, a three-channel drainage pipeline GPR inspection device is designed to realize the synchronous detection of the inside of the pipeline, the pipeline body, and the external environment of the pipeline, improving the detection depth and efficiency. According to the design scheme of the three-channel drainage pipeline GPR inspection device, the assembly of the device prototype was completed. The device contains three radar channels, the top of the main frequency of the antenna is 1.4 GHz, the two sides are 750 MHz, the video camera has a pixel count of 4 million, and the positioning accuracy is less than 1 mm, the waterproof grade is IP68, the detection accuracy of pipe deformation (slope) is 0.1°, the detection depth outside the pipe is 1.2 m, and the detection accuracy of corrosion thickness is 15 mm. In a practical application of the device, the Jianguomenqiao sewage pipeline in Beijing, China, was tested, resulting in the discovery of 87 defects, including 39 loose soil areas at the bottom of the pipe exterior, 40 void areas, and 8 cavities.

Perspective on pulse compression favorable infrared imaging techniques for non-destructive testing and evaluation of solids

Abstract Inspection reliability of sub-surface defects is imperative for safer functionality of critical components/materials/structures used in a wide variety of applications in various industries. The need for reliable, fast, remote, safe inspection and evaluation methods for detection of hidden defects increases in parallel with the demand for more sustainable solutions which helps in inherent modifications in design and manufacturing specifications. During in-service operation, the hidden defects are typically originated from various loading conditions leading to catastrophic failure. This perspective explores the best possible reliable, fast, remote, safe, and easy to implement for field inspection and evaluation experimental method and the associated post-processing approach using InfraRed Imaging (IRI) for Thermal Non-Destructive Testing and Evaluation (TNDT&E) of Glass Fibre Reinforced Polymer (GFRP) materials having delamination defects. This perspective further explores the state-of-the-art infrared imaging modalities used in TNDT&E by highlighting their advantages and limitations in terms of the detection sensitivity and depth resolvability to detect the subsurface defects located at interior of material. Most of the proposed experimental and post-processing techniques presented in the literature for TNDT&E, explore the observed spatial thermal contrast over the defective region of sample to provide the depth resolvability from the reconstructed thermograms even though it is obtained from the temporal data processing on the captured image sequence. This perspective provides an insight on the state-of-the-art research in the field of thermal/infrared non-destructive testing and evaluation and associated post-processing approaches to visualize the hidden subsurface defects not only resolved by spatial thermal gradients but also simultaneously provide temporal thermal gradients to locate defective regions.

Study of a Compton backscattering wall defects detection device using the Monte Carlo method

Abstract In view of the shortcomings of traditional wall defect detection methods, such as small detection range, poor accuracy, non-portable device, and so on, a wall defects detection device based on Compton backscattering technology is designed by Monte Carlo method, which is mainly used to detect the size and location information of defects in concrete walls. It mainly consists of two parts, the source container and the detection system: first, through the simulation and analysis of the parameters such as the receiving angle of the backscattered particles and the rear collimating material of the detector, the influence of the fluorescent X-ray peak of the detector collimating material on the backscattered particle counts is eliminated and the detected error is reduced; second, the ring array detector design, compared with single array detector and surface array detector, can facilitate real-time detection of defect orientation, expanding the single scan range and improving the detection efficiency. After simulation and comparative analysis, the relevant optimal parameters are obtained: the object is detected using a Cs-137 γ-ray source with an activity of 6 mCi, and a ring detector consisting of four 0.5-inch cube-shaped CsI scintillator detectors is placed at 150° to receive the backscattered photons. The simulation analysis using the Monte Carlo FLUKA program showed that the maximum depth of wall defect detection is 8 cm, the maximum error fluctuation range of defect depth and thickness is ±1 cm, the overall device weight is <20 kg, and the measurement time is <5 min.

Investigating the Potential of Cosmic-Ray Neutron Sensing for Estimating Soil Water Content in Farmland and Mountainous Areas

The conventional methods of estimating soil water content (SWC) are mainly based on in situ measurements at sampling points and remote sensing measurements over an entire region. In view of these methods, cosmic-ray neutron sensing (CRNS) has received increasing attention in recent years as a mesoscale, noncontact SWC estimation technology that can provide more accurate and timely estimates of SWC over a larger area. In this study, we estimated SWC using both CRNS and soil-mounted detectors in farmland and mountainous areas, and evaluated the accuracy of the estimations at two experimental sites. Ultra-rapid adaptable neutron-only simulation (URANOS) was used to simulate the detection radius and depth of the two experimental sites and to obtain the spatial weights of the CRNS footprint. The results show that the theoretical range of detection was reduced in farmland compared to mountainous areas during the experimental period, suggesting that farmland retained more SWC even with less precipitation. Spatial weights were simulated to calculate the SWC of sampling points, and the weighted and averaged SWC were then correlated with CRNS. The weighting calculation improves the accuracy of CRNS estimations, with a determination coefficient (R2) of 0.645 and a root mean square error (RMSE) of 0.046 cm3·cm−3 for farmland, and reproduces the daily dynamics of SWC. The R2 and RMSE in mountainous areas are 0.773 and 0.049 cm3·cm−3, respectively, and the estimation accuracy of CRNS cannot be improved by the weighting calculation. The estimation accuracy of CRNS is acceptable in both regions, but the mountainous terrain obstructs neutron transmission, causing a deviation between the actual and theoretical neutron footprints in mountainous areas. Thus, the accuracy of SWC estimation is limited in mountainous terrain. In conclusion, this study demonstrates that CRNS is suitable for use in farmland and mountainous areas and that further attention should be given to the effects of topography and vegetation when it is applied in mountainous environments.

Detectable depth of unexposed parathyroid glands using near-infrared autofluorescence imaging in thyroid surgery.

Near-infrared light can penetrate the fat or connective tissues overlying the parathyroid gland (PG), enabling early localization of the PG by near-infrared autofluorescence (NIRAF) imaging. However, the depth at which the PG can be detected has not been reported. In this study, we investigated the detectable depth of unexposed PGs using NIRAF during thyroidectomy. Fifty-one unexposed PGs from 30 consecutive thyroidectomy patients, mapped by an experienced surgeon (K.D. Lee) with the use of NIRAF imaging, were included. For NIRAF detection of PGs, a lab-built camera imaging system was used. Detectable depths of the unexposed PGs were measured using a Vernier caliper. The NIRAF images were classified as faint or bright depending on whether a novice could successfully interpret the image as showing the PG. Data on variables that may affect detectable depth and NIRAF intensity were collected. Detectable depth ranged between 0.35 and 3.05 mm, with a mean of 1.23 ± 0.73 mm. The average NIRAF intensity of unexposed PGs was 3.13 au. After dissection of the overlying tissue, the intensity of the exposed PG increased to 4.88 au (p < 0.001). No difference in NIRAF intensity between fat-covered (3.27 ± 0.90 au) and connective tissue-covered PGs (3.00 ± 1.23 au) was observed (p = 0.369). PGs covered by fat tissue (depth: 1.77 ± 0.67 mm) were found at deeper locations than those covered by connective tissue (depth: 0.70 ± 0.21 mm) (p < 0.001). The brightness of images of the faint group (2.14 ± 0.48 au) was on average 1.24 au lower than that of the bright group (3.38 ± 1.04 au) (p = 0.001). A novice successfully localized 80.4% of the unexposed PGs. Other variables did not significantly affect detectable depth. Unexposed PGs could be mapped using NIRAF imaging at a maximum depth of 3.05 mm and an average depth of 1.23 mm. A novice was able to localize the PGs before they were visible to the naked eye at a high rate. These results can be used as reference data for localization of unexposed PGs in thyroid surgery.

Characteristic Analysis of Cascading-Combination DC/DC Controlled-Source Circuit for Marine Electromagnetic Transmitter

Nowadays marine electromagnetic method is a geophysical method which can effectively explore natural gas hydrate (NGH) resources. This method acquires seafloor structure and distribution laws of mineral resources by transmitting high-power electromagnetic waves to the seafloor, and its detection depth relies on intensity of transmitted electromagnetic waves. At present, marine electromagnetic transmitter is inappropriate for transmission of high-power electromagnetic waves, and therefore, a novel DC/DC controlled-source circuit formed by input-series and output-parallel ZVZCS DC/DC controlled-source circuit units was proposed in this paper, and thus transformation of high-voltage and high-power electric energy was realized by using switching devices with low rated voltage and rated current. Common duty ratio control is adopted to construct small-signal mathematical model of cascading-combination ZVZCS DC/DC controlled-source circuit, influences of parameters on voltage balance at input side and current balance at output side are analyzed. Simulation and experimental results verify that the proposed circuit has favorable voltage and current sharing effect and could satisfy deep exploration requirements of marine electromagnetic transmitter with high transmission voltage.

Typical misinterpretations and scientific concepts in well-logging geologic studies

The method and theoretical system of well logging geology have been widely used in the fields of basic geology, petroleum geology and engineering geology, but the different response sensitivity of different well logging series to geological information and the mismatching between geophysical properties of multiple well logs and geological genesis of rocks frequently result in misunderstandings in the research process of well logging geology. Therefore, it is in an urgent need to analyze the typical misunderstanding cases in the research of well logging geology and explore the corresponding scientific ideas and countermeasures. After analyzing the typical misunderstandings in the research of well logging geology, this paper investigates vertical resolution scale of various logging series and its contradiction with detection depth and illustrates the importance of the integration of different scales of data. In addition, the factor inducing “fake logging data” and its influence on interpretation evaluation are clarified and a set of ideas for well logging evaluation of geological interpretation is put forward. And the following research results are obtained. First, the typical misunderstandings in the research of well logging geology can be classified into two categories, namely geological body interpretation misunderstanding and reservoir property parameter calculation misunderstanding. Second, special geological phenomena, such as high-density and high-resistivity mudstone can lead to logging data ambiguity, so attention shall be paid to petrophysical response mechanisms during geological logging interpretation. Third, to carry out well logging evaluation of unconventional oil and gas, it is necessary to integrate new technologies of electric imaging logging, dipole acoustic logging and nuclear magnetic resonance logging, and the calibration of core data and the integration of geological ideas can improve the interpretation accuracy. Fourth, In the process of borehole structural logging analysis, sedimentary response, geostress evaluation and fracture identification, geological ideas shall be integrated to avoid the logging interpretation misunderstanding caused by the same response of different geological phenomena in well logs. In conclusion, the dialectical and systematic thinking from geology to logging and then to geology, from practice to recognition and then to practice and from “a narrow view” to “a broad view” can provide a scientific ideas for the comprehensive research of well logging geology.

Experimental Research on Pipeline Corrosion Monitoring Technology Based on FSM

The field fingerprint method (FSM), a new online corrosion monitoring method, has a good application prospect. Of all the corrosion defects of pressure pipes, local small corrosion pits are the most commonly seen, and it is also the focus of FSM research. Based on the FSM method, this paper conducts experimental research on the common round and rectangular small corrosion pits of pressure pipes. The results show that the FSM method is sensitive to small volume defects, and the small corrosion pits with the minimum detectable depth of 0.5 mm can accurately reflect the defect growth process, this being of great significance to the safe operation of pipelines.

Multi-frequency broadband piezoelectric micromachined ultrasonic transducer utilizing helmholtz resonance

Piezoelectric micromachined ultrasonic transducers (PMUTs) with multi-frequency are appealing for ultrasonic applications, which can meet the requirements of large detection depth and high resolution simultaneously. Hence, a novel structure with series Helmholtz resonators under the PMUT membrane term as dual-cavity serial PMUT is presented, which utilizes both the piezoelectric effect and Helmholtz resonant effect to achieve multi-frequency and further widens the bandwidth. Based on the mechanism of vibration coupling between the membrane and Helmholtz resonator, a lumped model and an equivalent circuit model are formulated. Meanwhile, the finite element method (FEM) is developed to investigate the multi-resonance and make comprehensive validations. The results demonstrate that the multi-frequency is contributed by the induced acoustic impedance of the Helmholtz resonator, causing a simulated average sound pressure of up to 260 Pa/V at the resonance frequency and a 77% -6 dB frequency bandwidth in air by theoretical analysis and parameter optimization. The proposed dual-cavity serial PMUT provides a new approach for the application of high transmission power and broad bandwidth transducers.

Research and application of high-resolution micro-cylindrically focused logging tool

The unknown nature and complexity of non-uniform formations cause new difficulties and challenges to the accurate detection of electrical instruments in shallow formations. The micro-cylindrically focused logging tool (MCFL) can provide three original measurement curves, RB0, RB1, and RB2, with different detection depths, which reflect the flushing zone resistivity, mudcake resistivity, and mudcake thickness. In this study, the finite element method was used to model and analyze the micro-cylindrically focused logging tool tool in a three-dimensional non-uniform medium model. By converting the partial differential equation into a generalized polar problem, the logging response characteristics of the micro-cylindrically focused logging tool tool at different detection depths and ranges, mudcake thicknesses, flush zones, and mudcake resistivity contrasts were investigated. Inverse processing of the micro-cylindrically focused logging tool data using the least-squares method was used to obtain the flush zone resistivity, mudcake resistivity, and mudcake thickness, based on which the micropotential and microgradient curves were synthesized. In addition, a digital focusing method was proposed to improve the focusing accuracy and flexibility of the instrument, enhancing the performance of the micro-cylindrically focused logging tool. The optimized design of the focusing method significantly improved the detection performance of the pole plate. This plays an important role in the evaluation of thin layers and oil-water reservoirs.

Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning

Automatic inspection for crack detection and estimation of the crack depth is critical in assessing the damage and determining the appropriate method of repair in concrete structures. Most of the studies which have employed deep learning models for automatic inspection are limited to the detection and estimation of the width, length, area, and direction of cracks. The innovation of this study lies in developing a comprehensive automated crack detection and crack depth evaluation framework for concrete structures using images taken from portable devices. Firstly, a binary-class Convolutional Neural Network (CNN) model was developed to automatically detect the cracks on a concrete surface. Secondly, an integrated CNN model combining the convolutional feature extraction layers and regression models (RF and XGBoost) was developed to automatically predict the depth of the cracks. The proposed framework has been validated on a reinforced concrete (RC) slab. Results indicate the models are accurate and reliable for automated inspection of the cracks which could help in evaluating the condition of a concrete structure and choosing suitable repair methods.

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Under the same detection frequency and depth, when the center spacing of multiple defects is less than the resolution threshold determined by the Rayleigh criterion, it is challenging to achieve super-resolution imaging of multiple defects using the ultrasonic total focusing method (TFM). A multilevel deep learning network is proposed as a super-resolution reconstruction method for ultrasonic Lamb wave TFM images. The first-level network is a detection network that uses a Resnet50 with more convolutional layers to improve the linear expression capabilities of the neural network. It introduces residual structure to solve low accuracy issues of multi-convolutional layers so that the Resnet50 can accurately detect defects from TFM images. The second-level network is a super-resolution reconstruction network that uses a Deeplab v3+ with a dilated convolutional layer. It controls the receptive field without changing the image feature size of the TFM image. With this model, a super-resolution reconstruction of multiple defects with a center spacing less than the resolution threshold is realized by extracting the detailed features of defects in the TFM image. Experimental results show that when the defect center spacing is greater than the resolution threshold determined by the Rayleigh criterion, the super-resolution reconstruction method improves the calculation accuracy of the defect center spacing by 4.7% and the calculation accuracy of the defect area by 93.7% compared with TFM. When the defect spacing is less than the resolution threshold, the method can still identify and accurately calculate the center spacing of multiple defects.

Target Positioning for Complex Scenes in Remote Sensing Frame Using Depth Estimation Based on Optical Flow Information

UAV-based target positioning methods are in great demand in fields, such as national defense and urban management. In previous studies, the localization accuracy of UAVs in complex scenes was difficult to be guaranteed. Target positioning methods need to improve the accuracy with guaranteed computational speed. The purpose of this study is to improve the accuracy of target localization while using only UAV information. With the introduction of depth estimation methods that perform well, the localization errors caused by complex terrain can be effectively reduced. In this study, a new target position system is developed. The system has these features: real-time target detection and monocular depth estimation based on video streams. The performance of the system is tested through several target localization experiments in complex scenes, and the results proved that the system can accomplish the expected goals with guaranteed localization accuracy and computational speed.

Confronting turbidity, the major challenge for satellite-derived coastal bathymetry

Monitoring the complex seafloor morphology that drives the functioning of shallow coastal ecosystems is vital for assessing marine activities. Satellite-derived bathymetry (SDB) can provide a crucial dataset for creating the bathymetry maps needed to understand hazards and impacts produced by climate change in vulnerable coastal zones. SDB is effective in clear water, but still has limitations in application to areas with some turbidity. Here, using the twin satellites Sentinel-2A/B, we integrate water quality information from the satellite with a multi-temporal compositing method to demonstrate a potential for comprehensively operational bathymetric mapping over a range of environments. The automated compositing method diminishes the turbidity impact in addition to inferring the maximum detectable depth and removing optically deep-water areas. Examining a wide range of conditions along the Caribbean and eastern coast of the U.S. shows detailed bathymetry as deep as 30 m at 10 m spatial resolution with median errors <1 m when compared to high-resolution lidar surveys. These results demonstrate that the model adopted can provide useful bathymetry in areas that do not have consistently clear water and can be extended across multiple geographic regions and optical conditions at local, regional, and national scales.