Binning Procedure Research Articles

Robust Optical Physical Unclonable Function Based on Total Internal Reflection for Portable Authentication.

Physical unclonable functions (PUFs) utilize uncontrollable manufacturing randomness to yield cryptographic primitives. Currently, the fabrication of the most generally employed optical PUFs mainly depends on fluorescent, Raman, or plasmonic materials, which suffer inherent robustness issues. Herein, we construct an optical PUF with high environmental stability via total internal reflection (TIR-PUF) perturbed by randomly distributed polymer microspheres. The response image is transformed into encoded keys via an iterative binning procedure. The concentration of the polymer solution is optimized to debias the bit nonuniformity and maximize encoding capacity. The constructed TIR-PUF shows significantly high encoding capacity (2370) and markedly low total authentication error probability (1.614 × 10-23). The intra-Hamming distance is as low as 0.068, indicating the excellent readout reliability of TIR-PUF. The environmental stability of TIR-PUF has demonstrated promising results under a range of challenging conditions such as ultrasonic washing, high temperature, ultraviolet irradiation, and severe chemical environments. Moreover, the challenge-response pairs of our TIR-PUFs are demonstrated on an authentication system with low-power dissipation, lightweight components, and wireless imaging capture, rendering the possibility of portable authentication for practical applications.

Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression

Accurate estimation of precipitation at local to global scales can considerably enhance our understanding of climate system dynamics. While numerous precipitation products are available as indispensable tools for investigating precipitation and its associated processes, none can consistently provide the lowest estimation error across environmental conditions. The multiple source precipitation ensemble (MSPE) methods have been considered a vital solution. A new MSPE framework is proposed here, which simultaneously uses machine learning (ML) classification and regression techniques within an automatic workflow (MSPEaml). Six precipitation products and their ensembles based on different MSPE strategies were evaluated at 2365 gauged and 800 randomly selected ungauged sites over China. Results revealed significant precision inconsistencies among the products primarily due to their different data sources and retrieval algorithms; while MSPEaml can effectively reduce the random and classification errors of estimated precipitation according to the Kling-Gupta efficiency and Heidke skill score. The improvements demonstrated the unique features of MSPEaml, particularly the necessity of the joint use of ML classifiers and regressors and assigning spatiotemporal dynamic weights for merging precipitation data. Moreover, MSPEaml can substantially improve its generalizability through a simple binning procedure, making it applicable under more complex conditions. The varying contributions of predictor variables (indicated by Shapely values) in different ML models identified the complexity of the MSPE issue and further the importance of designing proper ML models according to specific targets. The proposed MSPE framework is expected to be a suitable solution for assembling multiple precipitation data sources with different time periods and scales.

VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes

BackgroundWith the advent of metagenomics, the importance of microorganisms and how their interactions are relevant to ecosystem resilience, sustainability, and human health has become evident. Cataloging and preserving biodiversity is paramount not only for the Earth’s natural systems but also for discovering solutions to challenges that we face as a growing civilization. Metagenomics pertains to the in silico study of all microorganisms within an ecological community in situ, however, many software suites recover only prokaryotes and have limited to no support for viruses and eukaryotes.ResultsIn this study, we introduce the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite developed to recover genomes from all domains. To our knowledge, VEBA is the first end-to-end metagenomics suite that can directly recover, quality assess, and classify prokaryotic, eukaryotic, and viral genomes from metagenomes. VEBA implements a novel iterative binning procedure and hybrid sample-specific/multi-sample framework that yields more genomes than any existing methodology alone. VEBA includes a consensus microeukaryotic database containing proteins from existing databases to optimize microeukaryotic gene modeling and taxonomic classification. VEBA also provides a unique clustering-based dereplication strategy allowing for sample-specific genomes and genes to be directly compared across non-overlapping biological samples. Finally, VEBA is the only pipeline that automates the detection of candidate phyla radiation bacteria and implements the appropriate genome quality assessments. VEBA’s capabilities are demonstrated by reanalyzing 3 existing public datasets which recovered a total of 948 MAGs (458 prokaryotic, 8 eukaryotic, and 482 viral) including several uncharacterized organisms and organisms with no public genome representatives.ConclusionsThe VEBA software suite allows for the in silico recovery of microorganisms from all domains of life by integrating cutting edge algorithms in novel ways. VEBA fully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions of VEBA to the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the flexibility to perform specific analytical tasks. VEBA allows for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets.

Analysis of the anaerobic digestion metagenome under environmental stresses stimulating prophage induction

BackgroundThe viral community has the potential to influence the structure of the microbiome and thus the yield of the anaerobic digestion process. However, the virome composition in anaerobic digestion is still under-investigated. A viral induction experiment was conducted on separate batches undergoing a series of DNA-damaging stresses, in order to coerce temperate viruses to enter the lytic cycle.ResultsThe sequencing of the metagenome revealed a viral community almost entirely composed of tailed bacteriophages of the order Caudovirales. Following a binning procedure 1,092 viral and 120 prokaryotic genomes were reconstructed, 64 of which included an integrated prophage in their sequence.Clustering of coverage profiles revealed the presence of species, both viral and microbial, sharing similar reactions to shocks. A group of viral genomes, which increase under organic overload and decrease under basic pH, uniquely encode the yopX gene, which is involved in the induction of temperate prophages. Moreover, the in-silico functional analysis revealed an enrichment of sialidases in viral genomes. These genes are associated with tail proteins and, as such, are hypothesised to be involved in the interaction with the host. Archaea registered the most pronounced changes in relation to shocks and featured behaviours not shared with other species. Subsequently, data from 123 different samples of the global anaerobic digestion database was used to determine coverage profiles of host and viral genomes on a broader scale.ConclusionsViruses are key components in anaerobic digestion environments, shaping the microbial guilds which drive the methanogenesis process. In turn, environmental conditions are pivotal in shaping the viral community and the rate of induction of temperate viruses. This study provides an initial insight into the complexity of the anaerobic digestion virome and its relation with the microbial community and the diverse environmental parameters.5iPig9MR3ASALE62Tsjw9nVideo

Disentangle genus microdiversity within a complex microbial community by using a multi-distance long-read binning method: example of Candidatus Accumulibacter.

SummaryComplete genomes can be recovered from metagenomes by assembling and binning DNA sequences into metagenome assembled genomes (MAGs). Yet, the presence of microdiversity can hamper the assembly and binning processes, possibly yielding chimeric, highly fragmented and incomplete genomes. Here, the metagenomes of four samples of aerobic granular sludge bioreactors containing Candidatus (Ca.) Accumulibacter, a phosphate‐accumulating organism of interest for wastewater treatment, were sequenced with both PacBio and Illumina. Different strategies of genome assembly and binning were investigated, including published protocols and a binning procedure adapted to the binning of long contigs (MuLoBiSC). Multiple criteria were considered to select the best strategy for Ca. Accumulibacter, whose multiple strains in every sample represent a challenging microdiversity. In this case, the best strategy relies on long‐read only assembly and a custom binning procedure including MuLoBiSC in metaWRAP. Several high‐quality Ca. Accumulibacter MAGs, including a novel species, were obtained independently from different samples. Comparative genomic analysis showed that MAGs retrieved in different samples harbour genomic rearrangements in addition to accumulation of point mutations. The microdiversity of Ca. Accumulibacter, likely driven by mobile genetic elements, causes major difficulties in recovering MAGs, but it is also a hallmark of the panmictic lifestyle of these bacteria.

Batch Assorting for Worker-Following Assortment Carts in Parallel-Aisle Order-Assorting Systems

This study introduces an order-assorting system (OAS) in a distribution center. The system supports assortments with worker-following carts. The workers and worker-following carts move during an order-assortment operation before which the binning operation splits the large-volume stock-keeping units (SKUs) into bins according to the number of aisles. We propose two mixed-integer programming models. The batching-only model (BOM) conducts the batching operation to shorten the total travel distance. The binning and batching model (BBM) assumes that all SKUs are split into bins according to the number of aisles and finds the optimal point between binning and batching. We also propose the route packing-based binning then batching (RPBB) heuristic to solve a large-sized BBM problem. RPBB consists of a binning procedure based on route packing (BPM-RP) and a batching procedure using a simple integer programming formulation. The results of the experiments evaluating the performance of the BBM and the RPBB heuristic show that the model and heuristic optimize the balance between binning and batching to reduce the total travel distance. In the large-sized problem, the RPBB obtains near-optimal solutions by the tight lower bound that shows 1.41-2.30% optimal gaps on average.

Weight-of-evidence through shrinkage and spline binning for interpretable nonlinear classification

In many practical applications, such as fraud detection, credit risk modeling or medical decision making, classification models for assigning instances to a predefined set of classes are required to be both precise and interpretable. Linear modeling methods such as logistic regression are often adopted since they offer an acceptable balance between precision and interpretability. Linear methods, however, are not well equipped to handle categorical predictors with high cardinality or to exploit nonlinear relations in the data. As a solution, data preprocessing methods such as weight of evidence are typically used for transforming the predictors. The binning procedure that underlies the weight-of-evidence approach, however, has been little researched and typically relies on ad hoc or expert-driven procedures. The objective in this paper, therefore, is to propose a formalized, data-driven and powerful method. To this end, we explore the discretization of continuous variables through the binning of spline functions, which allows for capturing nonlinear effects in predictor variables and yields highly interpretable predictors that take only a small number of discrete values. Moreover, we extend the weight-of-evidence approach and propose to estimate the proportions using shrinkage estimators. Together, this method offers an improved ability to exploit both nonlinear and categorical predictors to achieve increased classification precision while maintaining the interpretability of the resulting model and decreasing the risk of overfitting. We present the results of a series of experiments in fraud detection and credit risk settings, which illustrate the effectiveness of the presented approach.

Spectral binning of precomputed correlated-k coefficients

With the major increase in the volume of the spectroscopic line lists needed to perform accurate radiative transfer calculations, disseminating accurate radiative data has become almost as much a challenge as computing it. Considering that many planetary science applications are only looking for heating rates or mid-to-low resolution spectra, any approach enabling such computations in an accurate and flexible way at a fraction of the computing and storage costs is highly valuable. For many of these reasons, the correlated-k approach has become very popular. Its major weakness has been the lack of ways to adapt the spectral grid/resolution of precomputed k-coefficients, making it difficult to distribute a generic database suited for many different applications. Currently, most users still need to have access to a line-by-line transfer code with the relevant line lists or high-resolution cross sections to compute k-coefficient tables at the desired resolution. In this work, we demonstrate that precomputed k-coefficients can be binned to a lower spectral resolution without any additional assumptions, and show how this can be done in practice. We then show that this binning procedure does not introduce any significant loss in accuracy. Along the way, we quantify how such an approach compares very favorably with the sampled cross section approach. This opens up a new avenue to deliver accurate radiative transfer data by providing mid-resolution k-coefficient tables to users who can later tailor those tables to their needs on the fly. To help with this final step, we briefly present Exo_k, an open-access, open-source Python library designed to handle, tailor, and use many different formats of k-coefficient and cross-section tables in an easy and computationally efficient way.

BOB: a bi-level overlapped binning procedure for scene word binarization

Scene text analysis involves detecting and processing text/words in natural scene images for serving various purposes. This problem domain intrigues the research fraternity due to challenges like dealing with noise, blur, heterogeneous intensity variation, etc. The ultimate goal is making detected scene word recognizable by any standard Optical Character Recognition system, thereby necessitating effective scene word binarization. Several methods address scene text detection, but comparatively few addresses scene word binarization. These binarization methods, however, have limitations in robustness against image quality-based complexities thus causing low precision. Here, a novel approach is proposed for scene word binarization called Bi-level Overlapped Binning where intensities of color channels R, G and B are grouped or binned to generate several solutions in the form of binary images. The stable binary images are identified such that the image solutions from them can be classified as text or non-text using a standard classifier trained with some popular features. Finally, the resultant text solutions are combined probabilistically to get the binarized output. The proposed method is evaluated on standard datasets such as SVT, ICDAR-2003, ICDAR-2011 (Scene), ICDAR-2011 (BDI), KAIST and Total-Text achieving precisions 0.76, 0.87, 0.89, 0.85, 0.84 and 0.87 respectively, which are mostly better than that of the state-of-the-art.

Functional binning: improving convergence of Eulerian statistics from Lagrangian particle tracking

In the analysis of Lagrangian particle tracking data, ensemble averaging with spatial bins is used to generate Eulerian flow statistics. Due to the scattered nature of the particles over independent snapshots, the possible spatial resolution is directly dependent on the measured particle position accuracy and the amount of available data. This requires a balance between convergence of the underlying statistic and the bin resolution. Current binning approaches use the velocity information of the particle positions at single time steps directly and do not exploit the additional information available from the temporal filtering of the tracking process. We present a novel functional approach to the binning procedure that extracts all available information from the particle tracks and improves convergence speed. For a given experiment this allows for higher resolution of flow statistics than classical approaches or alternatively to reduce the necessary amount of data required for a given resolution. Furthermore, uncertainty measures from the particles position, velocity and acceleration can be propagated directly by weighting coefficients.

In situ Image Processing and Data Binning Strategy for Particle Engineering Applications

AbstractCrystallization control can be improved through real‐time monitoring technologies. Here, a workflow is demonstrated on rapid batch cooling crystallization of L‐glutamic acid. First, in situ images were generated using video microscopy sensors and analyzed, by employing a single, rapid macro code to extract particle data descriptors. A binning procedure (over time) was performed, where every data point represented the counts of particles within a specific size or shape range per 100 images. This binning method was found more informative in tracking of the populations compared to whole image averages or individual particle datapoints. This study provides a step‐by‐step guide towards improving mechanistic modeling, control via feedback, automation, and continuous manufacturing for Industry 4.0.

1179 Echocardiographic signs of aortic atherosclerosis in combination with clinical parameters for prediction of non-obstructive coronary atherosclerosis in men

Abstract Background Traditionally the gold standard for diagnosis of coronary artery disease (CAD) is detection of significant stenosis by coronary angiography. However, most myocardial infarctions result from ruptures of plaques that did not significantly compromise coronary lumen before the event. It was proved that significant stenosis is associated with stable CAD but non-significant coronary atherosclerosis is associated with unstable angina and acute coronary syndrome. Purpose To reveal predictors of non-significant coronary atherosclerosis using echocardiographic and clinical parameters in suspected CAD men without obstructive CAD by coronary angiography. Methods Out of 17.784 patients from coronary angiography database we selected 1.957 men without obstructive CAD. Selected men were divided into two groups randomly (1.389 - studied group and 568 – test group). The studied group was divided into subgroups: 304 men with non-significant coronary atherosclerosis (luminal narrowing or stenosis less than 30%) and 1.085 men with smooth coronary artery. Clinical and functional parameters of subgroups patients were compared. For selection of scoring variables, we used the values of the Cramer coefficient and the information value index - IV. All quantitative variables were converted into categorical using the binning procedure. Based on the values of WoE, the model of logistic regression was constructed using stepwise variable selection. Results The model includes independent variables with an average and high prognostic ability: echocardiographic signs of atherosclerosis of the ascending aorta, age, atherogenic index, arterial hypertension. By converting the coefficients of the logistic regression equation obtained into scores, a scoring model was developed to assess the probability of non-obstructive coronary atherosclerosis in men. Based on ROC analysis, in the studied group the specificity of the model for detection of nonsignificant coronary atherosclerosis was 67%, the sensitivity was 65%, the area under the ROC curve - 0.711. For the patients of the test group sensitivity was 68% and specificity - 63%, the area under the ROC curve - 0.687. Conclusion Nonsignificant coronary atherosclerosis in men with suspected CAD can be predicted before CAG by screening test based on echocardiographic signs of aortic atherosclerosis, age, atherogenic index and arterial hypertension.

Dynamics of H + HeH+(v = 0, j = 0) → H2+ + He: Insight on the Possible Complex-Forming Behavior of the Reaction

The H + HeH+→ He + H2+ reaction has been studied by means of a combination of theoretical approaches: a statistical quantum method (SQM), ring polymer molecular dynamics (RPMD), and the quasiclassical trajectory (QCT) method. Cross sections and rate constants have been calculated in an attempt to investigate the dynamics of the process. The comparison with previous calculations and experimental results reveals that despite the fact that statistical predictions seem to reproduce some of the overall observed features, the analysis at a more detailed state-to-state level shows noticeable deviations from a complex-forming dynamics. We find some differences in cross sections and rate constants obtained in the QCT calculation with a Gaussian binning procedure with respect to previous works in which the standard histogram binning was employed.

Assessing the attitudes of medical students towards psychiatry: A new paradigm.

Gauging the probability that medical students will select psychiatry as a career is a challenge, especially in Saudi Arabia, where the profession of psychiatry has still to gain ground. Thus, the aim of the current study was to investigate the attitudes of Saudi medical students towards psychiatry. A total of 317 medical students were recruited in a cross-sectional study. An ATP-30 questionnaire was used. In addition to the suggested cut-off point of 90 in previous studies, the data were also categorized using the visual binning procedure. To enhance the number of significant predictors and obtain more realistic results, an Ordinal Logistic Regression model was applied. The attitudes of medical students towards the three outcomes (dependent) variables; "Overall attitudes towards psychiatry, I want to be a psychiatrist, and Attitudes towards psychiatric treatment" varied across the five explanatory (predictor) variables, when assessed using Ordinal Logistic Regression. Age and gender proved significant with the three outcome variables, whereas Exposure to Psychiatric Clerkship identified "Overall attitudes towards psychiatry" and "Attitudes towards psychiatric treatment". Significant effects from a "Psychiatrist Relative" was found in "Overall attitudes towards psychiatry"and "I want to be a psychiatrist". The predictor variable; "Having a relative who is a psychiatric patient" was found to be significant only with, "I want to be a psychiatrist". The attitudes of medical students towards psychiatry can be predicted in the presence of specific factors. This is discussed in more detail in the relevant part of the study.

Diffusion tensor distribution imaging.

Conventional diffusion MRI yields voxel‐averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid‐state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with “size,” “shape,” and orientation dimensions. Here we combine our recent non‐parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a previously developed composite phantom with multiple isotropic and anisotropic components. We propose a compact format for visualizing two‐dimensional arrays of the distributions, new scalar parameters quantifying intra‐voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space.

Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea

Lagrangian tracking of passive tracers in a stochastic velocity field within a sequential ensemble data assimilation framework is challenging due to the exponential growth in the number of particles. This growth arises from describing the behavior of velocity over time as a set of possible combinations of the different realizations, before and after each assimilation cycle. This paper addresses the problem of efficiently advecting particles in stochastic flow fields, whose statistics are prescribed by an underlying ensemble, in a parallel computational framework (openMP). To this end, an efficient algorithm for forward and backward tracking of passive particles in stochastic flow-fields is presented. The algorithm, which employs higher order particle advection schemes, presents a mechanism for controlling the growth in the number of particles. The mechanism uses an adaptive binning procedure, while conserving the zeroth, first and second moments of probability (total probability, mean position, and variance). The adaptive binning process offers a tradeoff between speed and accuracy by limiting the number of particles to a desired maximum. To validate our method, we conducted various forward and backward particles tracking experiments within a realistic high-resolution ensemble assimilation setting of the Red Sea, focusing on the effect of the maximum number of particles, the time step, the variance of the ensemble, the travel time, the source location, and history of transport.

Further Tests of the Utility of Integrated Speed-Accuracy Measures in Task Switching.

Speed and accuracy of performance are central to many theoretical accounts of cognitive processing. In recent years, several integrated performance measures have been proposed. A comparative study of the available measures [Vandierendonck, A. (2017). A comparison of methods to combine speed and accuracy measures of performance: A rejoinder on the binning procedure. Behavior Research Methods, 49, 653–673. DOI: https://doi.org/10.3758/s13428-016-0721-5] concluded that three of the measures, namely inverse efficiency score, rate correct score, and linear integrated speed-accuracy score achieved a balanced integration of speed and accuracy. As a follow-up on that study, these three measures were examined in data analyses from 13 (published and unpublished) experiments in the context of task switching. The correlations of the effect sizes in these integrated scores with the effect sizes obtained in latency and accuracy were high, but varied across the three integrated measures. The efficiency to detect effects supported by the speed and accuracy data was examined by means of signal detection analyses. The three measures efficiently detected effects present in either speed or accuracy, but the rate correct score was less efficient than the other two measures and it signalled a larger number of strong effects unsupported by the speed and accuracy data. It is concluded that while the rate correct score is better avoided, and the usage of the inverse efficiency score should be restricted to data with low overall error rates, the linear integrated speed-accuracy score proves to be valid.

Automated data mining of secondary ion mass spectrometry spectra

AbstractTime of flight secondary ion mass spectrometry (ToF‐SIMS) allows the reliable analytical determination of organic and polymeric materials. Since a typical raw data may contain thousands of peaks, the amount of information to deal with is accordingly large, so that data reduction techniques become indispensable for extracting the most significant information from the given dataset. Here, the use of the wavelet‐principal component analysis–based signal processing of giant raw data acquired during ToF‐SIMS experiments is presented. The proposed procedure provides a straightforwardly “manageable” dataset without any binning procedure neither detailed integration. By studying the principal component analysis results, detailed and reliable information about the chemical composition of polymeric samples have been gathered.

Stratospheric aerosol data records for the climate change initiative: Development, validation and application to chemistry-climate modelling

This paper presents stratospheric aerosol climate records developed in the framework of the Aerosol_cci project, one of the 14 parallel projects from the ESA Climate Change Initiative. These data records were processed from a stratospheric aerosol dataset derived from the GOMOS experiment, using an inversion algorithm optimized for aerosol retrieval, called AerGOM. They provide a suite of aerosol parameters, such as the aerosol extinction coefficient at different wavelengths in the UV–visible range. The extinction record includes the total extinction as well as separate fields for liquid sulfate aerosols and polar stratospheric clouds (PSCs). Several additional fields (PSC flag, etc.) are also provided. The resulting stratospheric aerosol dataset, which spans the whole duration of the GOMOS mission (2002−2012), was validated using different reference datasets (lidar and balloon profiles).In the present paper, the emphasis is put on the extinction records. After a thorough analysis of the original AerGOM dataset, we describe the methodology used to construct the gridded CCI-GOMOS dataset and the resulting improvements on both the AerGOM algorithm and the binning procedure, in terms of spatio-temporal resolution, coverage and data quality.The extinction datasets were validated using lidar profiles from three ground-based stations (Mauna Loa, Garmisch-Partenkirchen, Dumont d'Urville). The median difference of the CCI-GOMOS (Level 3) extinction and ground-based lidar profiles is between ~15% and ~45% in the 16–21km altitude range, depending on the considered site and aerosol type.The CCI-GOMOS dataset was subsequently used, together with a MIPAS SO2 time series, to update a volcanic eruption inventory published previously, thus providing a more comprehensive list of eruptions for the ENVISAT period (2002–2012). The number of quantified eruptions increases from 102 to 230 in the updated inventory. This new inventory was used to simulate the evolution of the global radiative forcing by application of the EMAC chemistry-climate model. Results of this simulation improve the agreement between modelled global radiative forcing of stratospheric aerosols at about 100hPa compared to values estimated from observations. Medium eruptions like the ones of Soufriere Hills/Rabaul (2006), Sarychev (2009) and Nabro (2011) cause a forcing change from about −0.1W/m2 to −0.2W/m2.

Full Spectrum Analytical Channel Design with the Capacity/Supply Ratio (CSR)

Analytical channel design tools have not advanced appreciably in the last decades, and continue to produce designs based upon a single representative discharge that may not lead to overall sediment continuity. It is beneficial for designers to know when a simplified design may be problematic and to efficiently produce alternative designs that approximate sediment balance over the entire flow regime. The Capacity/Supply Ratio (CSR) approach—an extension of the Copeland method of analytical channel design for sand channels—balances the sediment transport capacity of a design reach with the sediment supply of a stable upstream reach over the entire flow duration curve (FDC) rather than just a single discharge. Although CSR has a stronger physical basis than previous analytical channel design approaches, it has not been adopted in practice because it can be a cumbersome and time-consuming iterative analysis without the use of software. We investigate eighteen sand-bed rivers in a comparison of designs based on the CSR approach and five single-discharge metrics: the effective discharge (Qeff) or discharge that transports the most sediment over time; the 1.5-year recurrence interval discharge (Q1.5); the bankfull discharge (Qbf); and the discharges associated with 50th (Qs50) and 75th (Qs75) percentiles of the cumulative sediment yield curve. To facilitate this analysis, we developed a novel design tool using the Visual Basic for Applications (VBA) programming language in Excel® to produce stable channel slope/width combinations based on the CSR methodology for both sand- and gravel-bed streams. The CSR Stable Channel Design Tool’s (CSR Tool) code structure was based on Copeland’s method in SAM and HEC-RAS (Hydrologic Engineering Center’s River Analysis System) and was tested with a single discharge to verify outputs. The Qs50 and Qs75 single-discharge designs match the CSR output most closely, followed by the Qbf, Qeff, and Q1.5. The Qeff proved to be the most inconsistent design metric because it can be highly dependent on the binning procedure used in the effectiveness analysis. Furthermore, we found that the more rigorous physical basis of the CSR analysis is potentially most important in designing “labile” channels with highly erodible substrate, high perennial flow “flashiness”, low width-to-depth ratio, and high incoming sediment load. The CSR Tool provides a resource for river restoration practitioners to efficiently utilize design techniques that can promote sediment balance in dynamic fluvial systems.