Multiple Depths Research Articles

Suitability of magnetometry to detect clandestine buried firearms from a controlled field site and numerical modeling

The detection of buried firearms remains a critical issue in law enforcement. We assess the suitability of magnetic gradiometers to detect buried rifles and handguns at multiple depths using numerical modeling and field investigations. Our simulation is based on a simple approach to characterize handguns and rifles as long magnetic dipoles with the firearm characterized by its magnetization, length, centre, azimuth and plunge which allows us to calculate their total magnetic field and gradient anomalies. We compare these synthetic data to field gradiometer data collected with a Gem Systems GSM-19GW Overhauser magnetometer at a field site near Toronto, Canada, where six firearms are buried. Our field magnetometer consists of two sensors with a relative vertical sepration of 0.55 m. We measure the largest anomaly (+/−20 nT) for a rifle at 0.6 m depth, and the smallest anomaly (+/−2 nT) for a handgun buried at 1.8 m depth. The measured anomalies spatially coincide with the locations of weapons while dipole anomalies align along the orientation of the firearms. Our modeling results show that vertically buried weapons produce significantly stronger anomalies than horizontal ones, and even slight tilts enhance the anomalies. We recommend a 0.25 m grid spacing to search for weapons using magnetometry. Our study shows that a range of firearms buried up to 1.8 m can be detected, suggesting that gradient magnetometers are useful tools in forensic weapon searches.

Ultrasound calibration with ladder phantom at multiple depths for breast biopsy navigation system

Ultrasound guided breast biopsy navigation system with a graphical user interface and a passive robotic needle holder is developed to increase the performance and reliability of the radiologist. Ultrasound calibration and tool tip calibration are required before using the system. A ladder phantom is developed to be used for ultrasound calibration in real time system with only one ultrasound image required. The passive robotic needle holder structure results in an identity matrix for the makes the rotation matrix; therefore, only translation and scaling are required in the system. This method can be applied to multiple ultrasound depths, which has a relationship at each depth and a relationship to the ultrasound image on the display. The results show high accuracy (<1 mm.) and rapid calibration (5–10 minutes) which is suitable for a real time system like a breast biopsy navigation system based on tests with a breast phantom.

Cr(VI) removal from aqueous solution using green adsorbents in continuous bed column – statistical and GA-ANN hybrid modelling

Mango, jackfruit, and rubber leaves showed potential for Cr(VI) elimination in a batch mode. The present study was performed in fixed bed downflow columns at multiple flow rates, bed depths, and influent concentrations for Cr(VI) elimination using the above green adsorbents. The experiments were performed at influent flow rates of 5 to 25 ml.min−1, concentrations of 5 to 80 ml.L−1, and bed depths of 3 to 9 cm. The adsorption capacity for reduced flow rates and concentrations were more effective. Well-known kinetic models had been used to fit the experimental data to determine their associated parameters. Thomas model was the best fit, especially for the mango leaf. The Thomas maximum adsorption capacity of mango, jackfruit, and rubber leaves was 69.52, 22.45, and 15.79 mg.g−1, respectively.The multiple linear regression and GA-ANN technique predicted Cr(VI) removal efficiency successfully with a high cross-correlation coefficient.

An open-source framework to model present and future marine species distributions at local scale

Species Distribution Models (SDMs) are useful tools to project potential future species distributions under climate change scenarios. Despite the ability to run SDMs in recent and reliable tools, there are some misuses and proxies that are widely practiced and rarely addressed together, particularly when dealing with marine species.In this paper, we propose an open-source framework that includes (i) a procedure for homogenizing occurrence data to reduce the influence of sampling bias, (ii) a procedure for generating pseudo-absences, (iii) a hierarchical-filter approach, (iv) full incorporation of the third dimension by considering climatic variables at multiple depths and (v) building of maps that predict current and potential future ranges of marine species. This framework is available for non-modeller ecologists interested in investigating future species ranges with a user-friendly script. We investigated the robustness of the framework by applying it to marine species of the Eastern English Channel. Projections were built for the middle and the end of this century under RCP2.6 and RCP8.5 scenarios.

Coupling dissolved oxygen microsensors measurements and heterogeneous respirometry for monitoring and modeling microbial activity within sulfide-oxidizing biofilms

A heterogeneous respirometer (HR) was coupled for the first time to a microelectrode monitoring system specifically designed for dissolved oxygen (DO) measuring within the biofilm. Monitoring of the oxygen concentration in the gas and liquid phases was complemented with pioneer monitoring of DO performed simultaneously and continuously at multiple biofilm depths in a linear array of eleven gold-disk electrodes of 50 µm-diameter. A set of respirometric tests performed at neutral pH and with initial gas phase concentrations of H2S ranging from 135 to 6720 ppmv were used to assess sulfide-oxidizing activity of a biofilm grown on 15.9 mm plastic Pall rings withdrawn from a biogas desulfurizing biotrickling filter. A mechanistic model for the description of multi-step sulfide oxidation within a biotrickling filter was improved considering heterogeneous biomass concentration and biomass activity distribution along the biofilm depth. A comprehensive description of physical, chemical and biological phenomena occurring throughout gas, liquid and biofilm phases resulted in an accurate prediction of system behavior. Model calibration using experimental data estimated a biomass concentration from 320.5 to 443.0 g VSS·L−1 as well as a decrease in the fraction of active biomass of 0.5, over the 600 µm thick biofilm. Model simulations accurately reproduced experimental respirometric profiles (NRMSE <10%), demonstrating that coupling HR and microelectrodes improved model predictions in comparison to sole gas or liquid phase measurements, thus contributing to a deeper knowledge of biofilms performance in trickled bed biological systems.

Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in the Water Column of Freshwater Lakes

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS.

Multiple-depth modeling of soil organic carbon using visible–near infrared spectroscopy

This paper evaluates the capability of visible-near-infrared (VIS-NIR) spectroscopy to estimate soil organic carbon (SOC) at multiple depths including 0–15, 15–40, 40–60, and 60–80 cm. Four modeling algorithms, namely partial least squares regression (PLSR), principal component regression (PCR), support vector regression (SVR), and random forest (RF) were implemented calibrated to process the spectroscopy data. Overall, 120 soil samples were taken from 30 profiles at the depth of 0–80 cm. We implemented the four models considering different pre-processing techniques including Savitzky-Golay first deviation (SGD), normalization (N), and standard normal variate transformation (SNV). Results revealed that the RF model outperformed other models and the highest accuracy was reached with no pre-processing for all depths excluding 40–60 cm, where the R2 and RMSE were between 0.55–0.77 and 0.75–0.84% respectively. For the depth of 40–60 cm, the maximum accuracy was observed when SGD pre-processing was applied, resulting in R2=0.73 and RMSE = 0.78%. Generally, our findings indicate that the spectral data can provide useful information to predict SOC at multiple depths.

Using storage selection functions to assess mixing patterns and water ages of soil water, evaporation and transpiration

Understanding flow and transport processes in the critical zone relies heavily on characterising the interactions between evaporation and vegetation uptake of soil water; however, the magnitude, sources, and ages of these water fluxes are rarely well-constrained. We adapted the StorAge Selection (SAS) function framework to estimate the residence time of stored water, and transit times of eco-hydrologic fluxes, at multiple soil depths in typical soil-vegetation units in the humid, energy-limited Scottish Highlands. Modelling water and stable isotope fluxes within the soil-vegetation units indicated that rapid movement of young water through the soils occurred at both sites, creating relatively stable water residence times in the soils with depth and time. Estimation of the evaporation profile had limited temporal variability with a high preference for near-surface water (0 – 5 cm soil depth, long-term mean age: 50 – 65 days) due to relatively frequent precipitation. Root uptake profiles revealed higher temporal variability, favouring deeper water (5 – 15 cm) during drier periods and near-surface (0 – 5 cm) during wet periods (long-term mean age: 6 – 15 days older than evaporation). The model structure provides a tool to help constrain water storage–flux–age interactions in the upper part of the critical zone and understand how soil–vegetation systems influence groundwater recharge and catchment-scale hydrology.

Applicability of temporal stability analysis in predicting field mean of soil moisture in multiple soil depths and different seasons in an irrigated vineyard

Accurately estimating field mean of soil water content (SWC) is important for irrigation management in an agricultural field. Temporal stability (TS) analysis has been widely applied to identify representative locations (RLs) for predicting spatial mean of SWC in natural landscapes. The objectives of the study were to examine TS patterns of SWC at various soil depths in different years, and to evaluate the performance of TS analysis in predicting field mean in an irrigated vineyard. Soil water content was measured regularly at 135 locations in the vineyard at soil depths of 0–20, 20–40, and 40–60 cm in 2012–2014. Temporal stability indices of mean relative difference (MRD), standard deviation of relative difference (SDRD), the comprehensive indicator (CI), and the mean absolute bias error (MABE) were obtained for each location at different depths in different seasons. The range of MRD and standard deviation of MRD (SDMRD) increased with soil depth for the three years. Inter-season correlations of the indices at the same depth were stronger than those between different depths of the same year. The RLs identified by CI and MABE were different between different soil depths and between the three seasons. Temporal stability analysis outperformed the random sampling method greatly, and on average one single RL could achieve RMSE about 2% and the maximum absolute error (MAE) less than 4% at a given sampling day during the validation period when the same year data were used to identify the RL. Estimation error could be reduced by 40% with the number of RLs increased from one to four. For most cases, four RLs identified by 2012 data were good enough to obtain RMSE and MAE less than 3% for the following two years. Soil texture information was found unable to identify RLs that could reliably estimate field mean. Temporal stability analysis is a valuable tool in identifying RLs to estimate field mean in an irrigated agricultural field, while intensive SWC sampling during half an irrigation cycle is needed to identify the RLs.

A framework for glass-box physics rule learner and its application to nano-scale phenomena

Attempts to use machine learning to discover hidden physical rules are in their infancy, and such attempts confront more challenges when experiments involve multifaceted measurements over three-dimensional objects. Here we propose a framework that can infuse scientists’ basic knowledge into a glass-box rule learner to extract hidden physical rules behind complex physics phenomena. A “convolved information index” is proposed to handle physical measurements over three-dimensional nano-scale specimens, and the multi-layered convolutions are “externalized” over multiple depths at the information level, not in the opaque networks. A transparent, flexible link function is proposed as a mathematical expression generator, thereby pursuing “glass-box” prediction. Consistent evolution is realized by integrating a Bayesian update and evolutionary algorithms. The framework is applied to nano-scale contact electrification phenomena, and results show promising performances in unraveling transparent expressions of a hidden physical rule. The proposed approach will catalyze a synergistic machine learning-physics partnership.

Investigating Plastic Accumulation in Sediments on the Banks of the Ohio River Near Wheeling, WV

Plastics are a global threat to biodiversity, especially aquatic ecosystems where plastics from roads and other sources wash into rivers and streams on their way to the ocean. We chose to examine sediments from the banks of the of the Ohio River near Wheeling, WV for plastic debris. At multiple locations we took samples of soil at multiple depths 0-5 cm, 5-10 cm, and 10-15 cm to quantify the abundance of plastics in the riverbank sediments. Sediment samples were collected in the field, placed in glass jars, and then transported to the lab for processing. Samples were washed thorough sieves to separate plastic particles into 3 size classes, &gt; 2.00 mm, 2.00 mm &lt;1000 µm, and1000 µm &lt; 250 µm. Material from each size class was floated in saltwater in order to separate plastics from organic matter. Plastics particles were then placed on gridded petri dish and counted under a microscope. Our research is ongoing, but by continuing the research it will expand our knowledge of how plastics are accumulating in the Ohio River.

Nutrients and Heavy Metals in Legacy Sediments: Concentrations, Comparisons with Upland Soils, and Implications for Water Quality

AbstractConcentrations of nutrients and heavy metals in streambank legacy sediments are needed to estimate watershed exports and to evaluate against upland inputs. Concentrations of nutrients and heavy metals were determined for legacy sediments in 15 streambanks across northeastern Maryland, southeastern Pennsylvania, and northern Delaware. Samples were collected from multiple bank depths from forested, agricultural, urban, and suburban sites. Analyses were performed for fine (&lt;63 μm) and coarse sediment fractions. Nutrient and heavy metal concentrations were significantly higher in fine than coarse legacy sediments and water extractable nutrient concentrations were significantly greater for fine sediments. Nutrient and heavy metal concentrations were highest in streambank legacy sediments associated with urban land use, but few differences were found with bank depth. Total N (40–3,970 mg/kg) and P (25–1,293 mg/kg) and bioavailable P (0.25–48.8 mg/kg) concentrations for legacy sediments were lower than those for upland soils. This suggests that legacy sediments could serve as sink or source of N and P depending on the redox conditions and stream water nutrient concentrations. However, despite low concentrations, caution should be exercised since streambank erosion and legacy sediment mass loadings could be high, these sediments are in immediate proximity of aquatic ecosystems, and biogeochemical transformations could result in release of the nutrients.

Focus-tunable fiber-laser ultrasound sensor for high-resolution linear-scanning photoacoustic computed tomography

In this study, we presented a high-resolution linear-scanning photoacoustic computed tomography (PACT) system with a focus-tunable fiber-laser ultrasound sensor. By bending the fiber laser into a curvature radius of 25 mm, an 85 μm × 150 μm in-plane resolution and a 300 μm elevational resolution were achieved. The elevational resolution was ∼3 times better than the previously reported value. Hair phantoms at multiple depths from 2 cm to 6 cm were clearly observed by tuning the bending curvature. Additionally, the fiber-laser-based PACT system showed its capability for high-resolution imaging of small animals based on the images of abdominal subcutaneous vasculatures from a rat. With the excitation light delivered by a fiber bundle and the ultrasound detected by a fiber laser, this all-fiber linear-scanning PACT system holds great potential for photoacoustic imaging in preclinical and clinical applications.

SweepCam - Depth-Aware Lensless Imaging Using Programmable Masks.

Lensless cameras, while extremely useful for imaging in constrained scenarios, struggle with resolving scenes with large depth variations. To resolve this, we propose imaging with a set of mask patterns displayed on a programmable mask, and introduce a computational focusing operator that helps to resolve the depth of scene points. As a result, the proposed imager can resolve dense scenes with large depth variations, allowing for more practical applications of lensless cameras. We also present a fast reconstruction algorithm for scene at multiple depths that reduces reconstruction time by two orders of magnitude. Finally, we build a prototype to show the proposed method improves both image quality and depth resolution of lensless cameras.

New capabilities of the Monte Carlo dose engine ARCHER-RT: Clinical validation of the Varian TrueBeam machine for VMAT external beam radiotherapy.

The Monte Carlo radiation transport method is considered the most accurate approach for absorbed dose calculations in external beam radiation therapy. In this study, an efficient and accurate source model of the Varian TrueBeam 6X STx Linac is developed and integrated with a fast Monte Carlo photon-electron transport absorbed dose engine, ARCHER-RT, which is capable of being executed on CPUs, NVIDIA GPUs, and AMD GPUs. This capability of fast yet accurate radiation dose calculation is essential for clinical utility of this new technology. This paper describes the software and algorithmic developments made to the ARCHER-RT absorbed dose engine. AMD's Heterogeneous-Compute Interface for Portability (HIP) was implemented in ARCHER-RT to allow for device independent execution on NVIDIA and AMD GPUs. Architecture-specific atomic-add algorithms have been identified and both more accurate single-precision and double-precision computational absorbed dose calculation methods have been added to ARCHER-RT and validated through a test case to evaluate the accuracy and performance of the algorithms. The validity of the source model and the radiation transport physics were benchmarked against Monte Carlo simulations performed with EGSnrc. Secondary dose-check physics plans, and a clinical prostate treatment plan were calculated to demonstrate the applicability of the platform for clinical use. Absorbed dose difference maps and gamma analyses were conducted to establish the accuracy and consistency between the two Monte Carlo models. Timing studies were conducted on a CPU, an NVIDIA GPU, and an AMD GPU to evaluate the computational speed of ARCHER-RT. Percent depth doses were computed for different field sizes ranging from 1.5cm2 ×1.5cm2 to 22cm2 ×40cm2 and the two codes agreed for all points outside high gradient regions within 3%. Axial profiles computed for a 10cm2 ×10cm2 field for multiple depths agreed for all points outside high gradient regions within 2%. The test case investigating the impact of native single-precision compared to double-precision showed differences in voxels as large as 71.47% and the implementation of KAS single-precision reduced the difference to less than 0.01%. The 3%/3mm gamma pass rates for an MPPG5a multileaf collimator (MLC) test case and a clinical VMAT prostate plan were 94.2% and 98.4% respectively. Timing studies demonstrated the calculation of a VMAT plan was completed in 50.3, 187.9, and 216.8s on an NVIDIA GPU, AMD GPU, and Intel CPU, respectively. ARCHER-RT is capable of patient-specific VMAT external beam photon absorbed dose calculations and its potential has been demonstrated by benchmarking against a well validated EGSnrc model of a Varian TrueBeam. Additionally, the implementation of AMD's HIP has shown the flexibility of the ARCHER-RT platform for device independent calculations. This work demonstrates the significant addition of functionality added to ARCHER-RT framework which has marked utility for both research and clinical applications and demonstrates further that Monte Carlo-based absorbed dose engines like ARCHER-RT have the potential for widespread clinical implementation.

A microdosimetric analysis of the interactions of mono-energetic neutrons with human tissue

Nuclear reactions induced during high-energy radiotherapy produce secondary neutrons that, due to their carcinogenic potential, constitute an important risk for the development of iatrogenic cancer. Experimental and epidemiological findings indicate a marked energy dependence of neutron relative biological effectiveness (RBE) for carcinogenesis, but little is reported on its physical basis. While the exact mechanism of radiation carcinogenesis is yet to be fully elucidated, numerical microdosimetry can be used to predict the biological consequences of a given irradiation based on its microscopic pattern of energy depositions. Building on recent studies, this work investigated the physics underlying neutron RBE by using the microdosimetric quantity dose-mean lineal energy (y‾D) as a proxy. A simulation pipeline was constructed to explicitly calculate the y‾D of radiation fields that consisted of (i) the open source Monte Carlo toolkit Geant4, (ii) its radiobiological extension Geant4-DNA, and (iii) a weighted track-sampling algorithm. This approach was used to study mono-energetic neutrons with initial kinetic energies between 1 eV and 10 MeV at multiple depths in a tissue-equivalent phantom. Spherical sampling volumes with diameters between 2 nm and 1 μm were considered. To obtain a measure of RBE, the neutron y‾D values were divided by those of 250 keV X-rays that were calculated in the same way. Qualitative agreement was found with published radiation protection factors and simulation data, allowing for the dependencies of neutron RBE on depth and energy to be discussed in the context of the neutron interaction cross sections and secondary particle distributions in human tissue.

Bacterial communities and nitrogen transformation genes in streambank legacy sediments and implications for biogeochemical processing

Streambank legacy sediments may be important sources of sediment and nutrients from Mid-Atlantic watersheds. However, little is known about the nutrient processing roles of microorganisms that inhabit legacy sediments, let alone their composition, diversity, and distributions. In this study, we sampled 15 streambanks at multiple depths throughout four watersheds in the Mid-Atlantic Region of the USA. High throughput sequencing of 16S ribosomal RNA genes indicated that streambank microbial community composition varied within site depth and across contemporary land uses. Collectively, the most abundant microbial taxa in legacy sediments included Acidobacteria (25–45%), Proteobacteria (15–40%), Nitrospirae (2–10%), Chloroflexi (1–5%), and Actinobacteria (1–10%). Bacterial community composition was distinct between agriculture and urban sites as well as suburban and urban sites. There was significant variation in community composition between the top (1–25%), upper-middle (26–50%), and bottom layers (76–100%) of sediments, while the relative abundances differed between layers for only Acidobacteria and Proteobacteria. Several streambank chemistry variables (metals, %TC, and %TN) had weak positive correlations with community composition. Compared to ammonia-oxidizing bacteria, nitrifying archaea were more predominant. This study provides the first insights into detailed microbial composition of legacy sediments and identifies environmental drivers for community structure and nitrogen processing. Future studies should consider exploring the role of this unique microbial environment for nutrient processing and leaching from legacy sediments and its implications for watershed water quality.

Imaging and Tracking Through Scattering Medium With Low Bit Depth Speckle

Optical imaging/tracking through scattering medium is a difficult challenge. We demonstrate a method using speckles with multiple bit depths to image objects through scattering medium. The image of the object can be reconstructed even if the speckles are binary. Speckles with different bit depths are obtained by quantifying the original speckle. And we discuss the application of binarized speckles in tracking moving object. The binarized speckles can be used to recover the track with high accuracy. The range of the object's motion can be much larger than the range of memory effect, as long as the object's size and the moving step are within the range of memory effect. Imaging and tracking with low bit depth have the potential to reduce the hardware requirements of camera and storage space of images.

A Survey of Microbial Communities on Dry-Aged Beef in Commercial Meat Processing Facilities

Many artisanal meat professionals believe that the microbial populations on the outer crust of dry-aged beef contribute to variation in sensory profiles; however, to date there is minimal information about the microbes themselves that grow on commercially produced dry-aged beef. The microbiome of dry-aged beef bone-in strip loins (Institutional Meat Purchase Specifications #175) from 5 commercial dry aging facilities, including one utilizing ultraviolet light treatment, were surveyed to assess the microbial populations residing on and within each subprimal. Each strip loin was sampled at multiple spatial locations and depths, and the microbial sequences present in the samples were identified using a nextgeneration sequencing approach. Insufficient microbial DNA was isolated from ultraviolet-light-treated strip loins, indicating that this treatment eliminates all or most microbial growth on the meat. Sequencing results indicated that each establishment was producing meat with different microbial communities, based on Permutational Multivariate Analysis of Variance (P &amp;lt; 0.01) and clustering in the Principal Coordinates Analysis plot of Jaccard distances. The position on strip loins from which samples were taken had negligible influence on microbial community structure. Aging facility, and the relative unique environmental conditions within, was determined to be the only observed driver of community structure. Notable operational taxonomic units (OTUs) detected included the spoilage-associated bacterium Pseudomonas fragi and the fungal species Debaryomyces udenii and Penicillium polonicum. An OTU identified as Mucor sp. PG272 was found to be present in over 75% of all samples. This OTU may represent a species similar to Thamnidium, a mold that has been associated with product quality. This study established a general core microbiome for dry-aged beef observed in commercial facilities, variations of which may—as future research could indicate—contribute to distinct sensory properties.

Calculation of Multiple Critical Depths in Open Channels Using an Adaptive Cubic Polynomials Algorithm

A method for the calculation of multiple critical depths in compound and natural channels, using an adaptive cubic polynomials algorithm (ACPA), is presented in this paper. The algorithm is based on the approximation of the specific energy with multiple cubic polynomials. The roots of these polynomials’ derivatives are determined to calculate all local minima and maxima. These extremities yield the critical depths. Furthermore, the Froude number can be calculated at any elevation by applying a simple formula after calculating the derivative of the corresponding polynomial, which contains the given elevation. The algorithm developed was tested on various compound and natural channels. Its results were then compared with the results provided by the HEC-RAS (Hydrologic Engineering Center – River Analysis System) computer program, proving that in some cases ACPA results were more accurate than those of HEC-RAS. This has to do with the fact that HEC-RAS algorithm determines a single critical depth and is better fitted to simple prismatic channels. On the other hand, the ACPA algorithm is able to calculate all critical depths of a natural or compound channel, providing thus more accurate results.