Gradient Paths Research Articles

ESFD-YOLOv8n: Early Smoke and Fire Detection Method Based on an Improved YOLOv8n Model

Ensuring fire safety is essential to protect life and property, but modern infrastructure and complex settings require advanced fire detection methods. Traditional object detection systems, often reliant on manual feature extraction, may fall short, and while deep learning approaches are powerful, they can be computationally intensive, especially for real-time applications. This paper proposes a novel smoke and fire detection method based on the YOLOv8n model with several key architectural modifications. The standard Complete-IoU (CIoU) box loss function is replaced with the more robust Wise-IoU version 3 (WIoUv3), enhancing predictions through its attention mechanism and dynamic focusing. The model is streamlined by replacing the C2f module with a residual block, enabling targeted feature extraction, accelerating training and inference, and reducing overfitting. Integrating generalized efficient layer aggregation network (GELAN) blocks with C2f modules in the neck of the YOLOv8n model further enhances smoke and fire detection, optimizing gradient paths for efficient learning and high performance. Transfer learning is also applied to enhance robustness. Experiments confirmed the excellent performance of ESFD-YOLOv8n, outperforming the original YOLOv8n by 2%, 2.3%, and 2.7%, with a mean average precision (mAP@0.5) of 79.4%, precision of 80.1%, and recall of 72.7%. Despite its increased complexity, the model outperforms several state-of-the-art algorithms and meets the requirements for real-time fire and smoke detection.

Uncovering underlying physical principles and driving forces of cell differentiation and reprogramming from single-cell transcriptomics

Recent advances in single-cell sequencing technology have revolutionized our ability to acquire whole transcriptome data. However, uncovering the underlying transcriptional drivers and nonequilibrium driving forces of cell function directly from these data remains challenging. We address this by learning cell state vector fields from discrete single-cell RNA velocity to quantify the single-cell global nonequilibrium driving forces as landscape and flux. From single-cell data, we quantified the Waddington landscape, showing that optimal paths for differentiation and reprogramming deviate from the naively expected landscape gradient paths and may not pass through landscape saddles at finite fluctuations, challenging conventional transition state estimation of kinetic rate for cell fate decisions due to the presence of the flux. A key insight from our study is that stem/progenitor cells necessitate greater energy dissipation for rapid cell cycles and self-renewal, maintaining pluripotency. We predict optimal developmental pathways and elucidate the nucleation mechanism of cell fate decisions, with transition states as nucleation sites and pioneer genes as nucleation seeds. The concept of loop flux quantifies the contributions of each cycle flux to cell state transitions, facilitating the understanding of cell dynamics and thermodynamic cost, and providing insights into optimizing biological functions. We also infer cell-cell interactions and cell-type-specific gene regulatory networks, encompassing feedback mechanisms and interaction intensities, predicting genetic perturbation effects on cell fate decisions from single-cell omics data. Essentially, our methodology validates the landscape and flux theory, along with its associated quantifications, offering a framework for exploring the physical principles underlying cellular differentiation and reprogramming and broader biological processes through high-throughput single-cell sequencing experiments.

A new lightweight network for efficient UAV object detection

Optimizing the structure of deep neural networks is essential in many applications. Especially in the object detection tasks of Unmanned Aerial Vehicles. Due to the constraints of the onboard platform, a more efficient network is required to meet practical demands. Nevertheless, existing lightweight detection networks exhibit excessive redundant computations and may yield in a certain level of accuracy loss. To address these issues, this paper proposes a new lightweight network structure named Cross-Stage Partially Deformable Network (CSPDNet). The initial proposal consists of a Deformable Separable Convolution Block (DSCBlock), separating feature channels, greatly reducing the computational load of convolution, and applying adaptive sampling to the separated feature map. Subsequently, to establish information interaction between feature layers, a channel weighting module is proposed. This module calculates weights for the separated feature map, facilitating information exchange across channels and resolutions. Moreover, it compensates for the effect of point-wise (1 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 1) convolutions, filtering out more important feature information. Furthermore, a new CSPDBlock is designed, primarily composed of DSCBlock, establishing multidimensional feature correlations for each separated feature layer. This approach improves the ability to capture critical feature information and reconstruct gradient paths, thereby preserving detection accuracy. The proposed technology achieves a balance between model parameter size and detection accuracy. Furthermore, experimental results on object detection datasets demonstrate that our designed network, using fewer parameters, achieves competitive detection performance results compared to existing lightweight networks YOLOv5n, YOLOv6n, YOLOv8n, NanoDet and PP-PicoDet. The optimization effect of the designed CSPDBlock, using the VisDrone dataset, is validated when incorporated into advanced detection algorithms YOLOv5m, PPYOLOEm, YOLOv7, RTMDetm and YOLOv8m. In more detail, by incorporating the designed modules it was achieved that the parameters were reduced by 10–20% while almost maintaining detection accuracy.

A Lightweight Remote Sensing Small Target Image Detection Algorithm Based on Improved YOLOv8.

In response to the challenges posed by small objects in remote sensing images, such as low resolution, complex backgrounds, and severe occlusions, this paper proposes a lightweight improved model based on YOLOv8n. During the detection of small objects, the feature fusion part of the YOLOv8n algorithm retrieves relatively fewer features of small objects from the backbone network compared to large objects, resulting in low detection accuracy for small objects. To address this issue, firstly, this paper adds a dedicated small object detection layer in the feature fusion network to better integrate the features of small objects into the feature fusion part of the model. Secondly, the SSFF module is introduced to facilitate multi-scale feature fusion, enabling the model to capture more gradient paths and further improve accuracy while reducing model parameters. Finally, the HPANet structure is proposed, replacing the Path Aggregation Network with HPANet. Compared to the original YOLOv8n algorithm, the recognition accuracy of mAP@0.5 on the VisDrone data set and the AI-TOD data set has increased by 14.3% and 17.9%, respectively, while the recognition accuracy of mAP@0.5:0.95 has increased by 17.1% and 19.8%, respectively. The proposed method reduces the parameter count by 33% and the model size by 31.7% compared to the original model. Experimental results demonstrate that the proposed method can quickly and accurately identify small objects in complex backgrounds.

Visible and Infrared Image Fusion of Forest Fire Scenes Based on Generative Adversarial Networks with Multi-Classification and Multi-Level Constraints

Aimed at addressing deficiencies in existing image fusion methods, this paper proposed a multi-level and multi-classification generative adversarial network (GAN)-based method (MMGAN) for fusing visible and infrared images of forest fire scenes (the surroundings of firefighters), which solves the problem that GANs tend to ignore visible contrast ratio information and detailed infrared texture information. The study was based on real-time visible and infrared image data acquired by visible and infrared binocular cameras on forest firefighters’ helmets. We improved the GAN by, on the one hand, splitting the input channels of the generator into gradient and contrast ratio paths, increasing the depth of convolutional layers, and improving the extraction capability of shallow networks. On the other hand, we designed a discriminator using a multi-classification constraint structure and trained it against the generator in a continuous and adversarial manner to supervise the generator, generating better-quality fused images. Our results indicated that compared to mainstream infrared and visible image fusion methods, including anisotropic diffusion fusion (ADF), guided filtering fusion (GFF), convolutional neural networks (CNN), FusionGAN, and dual-discriminator conditional GAN (DDcGAN), the MMGAN model was overall optimal and had the best visual effect when applied to image fusions of forest fire surroundings. Five of the six objective metrics were optimal, and one ranked second-to-optimal. The image fusion speed was more than five times faster than that of the other methods. The MMGAN model significantly improved the quality of fused images of forest fire scenes, preserved the contrast ratio information of visible images and the detailed texture information of infrared images of forest fire scenes, and could accurately reflect information on forest fire scene surroundings.

SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection

An improved YOLOv8s-based method is proposed to address the challenge of accurately recognizing tiny objects in remote sensing images during practical human-computer interaction. In detecting tiny targets, the accuracy of YOLOv8s is low because the downsampling module of the original YOLOv8s algorithm causes the network to lose fine-grained feature information, and the neck network feature information needs to be sufficiently fused. In this method, the strided convolution module in YOLOv8s is replaced with the SPD-Conv module. By doing so, the feature map undergoes downsampling while preserving fine-grained feature information, thereby improving the learning and expressive capabilities of the network and enhancing recognition accuracy. Meanwhile, the path aggregation network is substituted with the SPANet structure, which facilitates the acquisition of more prosperous gradient paths. This substitution enhances the fusion of feature maps at various scales, reduces model parameters, and further improves detection accuracy. Additionally, it enhances the network’s robustness to complex backgrounds. Experimental verification is conducted on the following two intricate datasets containing tiny objects: AI-TOD and TinyPerson. A comparative analysis with the original YOLOv8s algorithm reveals notable enhancements in recognition accuracy. Specifically, under real-time performance constraints, the proposed method yields a 4.9% and 9.1% improvement in mAP0.5 recognition accuracy for AI-TOD and TinyPerson datasets, respectively. Moreover, the recognition accuracy for mAP0.5:0.95 is enhanced by 3.4% and 3.2% for the same datasets, respectively. The results indicate that the proposed method enables rapid and accurate recognition of tiny objects in complex backgrounds. Furthermore, it demonstrates better recognition precision and stability than other algorithms, such as YOLOv5s and YOLOv8s.

Gesture Detection and Recognition Based on Object Detection in Complex Background

In practical human–computer interaction, a hand gesture recognition method based on improved YOLOv5 is proposed to address the problem of low recognition accuracy and slow speed with complex backgrounds. By replacing the CSP1_x module in the YOLOv5 backbone network with an efficient layer aggregation network, a richer combination of gradient paths can be obtained to improve the network’s learning and expressive capabilities and enhance recognition speed. The CBAM attention mechanism is introduced to filtering gesture features in channel and spatial dimensions, reducing various types of interference in complex background gesture images and enhancing the network’s robustness against complex backgrounds. Experimental verification was conducted on two complex background gesture datasets, EgoHands and TinyHGR, with recognition accuracies of mAP0.5:0.95 at 75.6% and 66.8%, respectively, and a recognition speed of 64 FPS for 640 × 640 input images. The results show that the proposed method can recognize gestures quickly and accurately with complex backgrounds, and has higher recognition accuracy and stronger robustness compared to YOLOv5l, YOLOv7, and other comparative algorithms.

Gradient Matters: Designing Binarized Neural Networks via Enhanced Information-Flow.

Binarized neural networks (BNNs) have drawn significant attention in recent years, owing to great potential in reducing computation and storage consumption. While it is attractive, traditional BNNs usually suffer from slow convergence speed and dramatical accuracy-degradation on large-scale classification datasets. To minimize the gap between BNNs and deep neural networks (DNNs), we propose a new framework of designing BNNs, dubbed Hyper-BinaryNet, from the aspect of enhanced information-flow. Our contributions are threefold: 1) Considering the capacity-limitation in the backward pass, we propose an 1-bit convolution module named HyperConv. By exploiting the capacity of auxiliary neural networks, BNNs gain better performance on large-scale image classification task. 2) Considering the slow convergence speed in BNNs, we rethink the gradient accumulation mechanism and propose a hyper accumulation technique. By accumulating gradients in multiple variables rather than one as before, the gradient paths for each weight increase, which escapes BNNs from the gradient bottleneck problem during training. 3) Considering the ill-posed optimization problem, a novel gradient estimation warmup strategy, dubbed STE-Warmup, is developed. This strategy prevents BNNs from the unstable optimization process by progressively transferring neural networks from 32-bit to 1-bit. We conduct evaluations with variant architectures on three public datasets: CIFAR-10/100 and ImageNet. Compared with state-of-the-art BNNs, Hyper-BinaryNet shows faster convergence speed and outperforms existing BNNs by a large margin.

DAMQT 3: Advanced suite for the analysis of molecular density and related properties in large systems

A new version of the DAMQT package specially developed for large systems is reported. The graphical part has been entirely redesigned, using new OpenGL libraries (versions 3.3 or higher) for 3D display. Several 2D plotters and 3D viewers can be launched now in the same session and more than one molecule can be loaded in the same 3D window. Algorithms have been rescaled and modified to work with densities coming from ZDO computations in very big molecular systems (up to thousands of atoms) at a very moderate cost. New functionalities have been added including computation of molecular electrostatic potential over a molecular surface determined as a user-defined density isosurface. The method of Electrostatics for Intermolecular Complexation has been added to the package to serve as an auxiliary tool for cluster geometry optimization. Examples are provided which prove the good performance of the algorithms. Program summaryProgram Title: DAMQT_3CPC Library link to program files:https://doi.org/10.17632/2rxvgbsnhx.2Licensing provisions: GPLv3Programming language: Fortran90, C++ and PythonSupplementary material: Quick-start guide and User's manual in PDF format included in the package. User's manual is also accessible from the GUI.External routines/libraries: Qt (5.10 or higher), OpenGL (3.3 or higher), ffmpeg (3.4 or higher), OpenBabel (2.3 or higher, optional)Nature of problem: Analysis and visualization of the molecular electron density, electrostatic potential, critical points, gradient paths, atomic basins, electric field and Hellmann-Feynman forces on nuclei, clusters optimization with EPIC.Solution method: Molecular electron density is partitioned into (pseudo)atomic fragments by means of the method of Deformed Atoms in Molecules [1]. Electron densities of the fragments are expanded as a series of spherical harmonics times radial factors. The partition is used for defining molecular density deformations and for the fast calculation of several properties associated with density, including electrostatic potential, electric field and Hellmann-Feynman forces over nuclei. Exploration of density and potential topology is facilitated, and the computation of electrostatic potential over an isodensity surface is implemented. Cluster optimization facility with EPIC is also implemented.Additional comments including restrictions and unusual features: Density matrix must come from an LCAO calculation (any computational level) with spherical (not Cartesian) Slater or Gaussian functions.The program contains an OPEN statement to binary files (stream) in several files. This statement does not have a standard syntax in Fortran 90. Two possibilities are considered in conditional compilation: Intel's ifort and Fortran2003 standard. This latter is applied to compilers other than ifort (gfortran uses this one, for instance).

A Subspace-Inclusive Sampling Method for the Computational Design of Compositionally Graded Alloys

Abstract Compositionally graded alloys, a subclass of functionally graded materials (FGMs), utilize localized variations in composition with a single metal part to achieve higher performance than traditional single material parts. In previous work [Kirk, T., Galvan, E., Malak, R., and Arroyave, R., 2018, “Computational Design of Gradient Paths in Additively Manufactured Functionally Graded Materials,” J. Mech. Des., 140, p. 111410. 10.1115/1.4040816], the authors presented a computational design methodology that avoids common issues which limit a gradient alloy’s feasibility, such as deleterious phases, and optimizes for performance objectives. However, the previous methodology only samples the interior of a composition space, meaning designed gradients must include all elements in the space throughout the gradient. Because even small amounts of additional alloying elements can introduce new deleterious phases, this characteristic often neglects potentially simpler solutions to otherwise unsolvable problems and, consequently, discourages the addition of new elements to the state space. The present work improves upon the previous methodology by introducing a sampling method that includes subspaces with fewer elements in the design search. The new method samples within an artificially expanded form of the state space and projects samples outside the true region to the nearest true subspace. This method is evaluated first by observing the sample distribution in each subspace of a 3D, 4D, and 5D state space. Next, a parametric study in a synthetic 3D problem compares the performance of the new sampling scheme to the previous methodology. Lastly, the updated methodology is applied to design a gradient from stainless steel to equiatomic NiTi that has practical uses such as embedded shape memory actuation and for which the previous methodology fails to find a feasible path.

Symmetrical circulation gradient color system construction and gradient color yarn spun by a three-channel numerical control spinning system

Aiming at the technical drawback that traditional spinning technology cannot accurately control the color distribution of yarn and spin gradient color yarn, a three-channel numerical control spinning mechanism that regulates yarn color gradient online is put forward, and gradient color yarn is successfully produced. The coupling-combination color mixing mode of ternary color fibers is innovatively designed, and the mixing ratio matrix, mixing chromatography matrix and mixing chromatography, including all mixing samples, are constructed. Three kinds of gradient paths, namely the row gradient, column gradient and diagonal gradient, are planned based on the mixing ratio matrix, and the corresponding gradient chromatography matrix and gradient chromatography are given. Taking green, magenta and yellow rovings as an example, a three-channel numerical control spinning process was designed and three types of 16 kinds of gradient color yarns were spun. The results show that the knitted fabric of the gradient yarn spun by digitally adjusting the yarn blending ratio shows a periodic natural and soft gradient color. The mechanical properties and appearance quality of the gradient color yarn are close to the corresponding indexes of color-spun yarn, but there is still room for improvement.

Steric paths in confined hydrogen molecule inside carbon nanorings and fullerenes

Steric molecular graph is introduced as gradient paths connecting critical points responsible for depletion of charge density due to steric deformation density. Since steric repulsion dominates intermolecular interaction in confined systems, surrounded and encapsulated hydrogen molecule inside carbon nanorings and fullerenes are considered as confined systems, respectively. It is shown that structure of steric graph is consistent with the confinement dimensionality, i.e., ring- and cage-type steric graph appear for two- and three-dimensional confinements, respectively.

СТАТИСТИЧЕСКИЙ СТЕГАНОАНАЛИЗ ФОТОРЕАЛИСТИЧНЫХ ИЗОБРАЖЕНИЙ С ИСПОЛЬЗОВАНИЕМ ГРАДИЕНТНЫХ ПУТЕЙ

Purpose of the article is experimentally test the efficiency of the feature vector based on gradient paths in the spatial domain of the image. Research method is a comparison of steganalytical feature vectors based on the mean square error and the coefficient of determination obtained using SVM-regression in Matlab. The dataset is formed by automating the freeware steganoprograms that implement embedding into the spatial area of the image with sequential and pseudorandom selection of a pixel for embedding. Results of the study: the optimal parameters of the algorithm for seeking gradient paths from the point of view of embedding detection are experimentally obtained. The results of applying machine learning models are obtained and analyzed, the optimal scale of the SVM-regression kernel is determined. The computation durations of feature vectors obtaining, models training, recognizing containers are calculated. It is shown experimentally that the gradient paths feature vector is expedient to use for solving problems where it is necessary to vary the detection accuracy depending on functioning capacity of system, because the proposed feature vector allows to determine the dimension / accuracy ratio. Also, by experiment, a complex 20D vector is selected from several one-dimensional quantitative steganodetectors and the gradient paths feature vector. The effectiveness of result vector is comparable to the 686D feature vector SPAM.

Serialized gradient chromatography for the digital blending of colored fibers and spinning of gradient colored yarn

Gradient colored yarns are manufactured by controlling the blending ratios of three-primary-colored fibers in a slight distribution of gradients along the yarn length, thereby resulting in a continuous natural variation in mixed colors of the fibers throughout the whole yarn. The spinning of gradient colored yarns still remains a challenge, which requires the reliance on digital blending theory of colored fibers and the supporting of multi-channel computer numerical control (CNC) spinning technique. This paper constructed a three-primary-colored fiber gridded color mixing model and its mass mixing matrix and color mixing chromatography matrix by mass discretization and coupling pairing with a 10% gradient for the three-primary-colored fibers. With the aim of continuous natural gradient of mixed colors, the blending ratio gradient path of three-primary-colored fibers was planned based on the mass mixing matrix, and a method of regulating the gradient of color difference between adjacent color segments was proposed. In order to realize the natural gradient of color of the forming yarn, the spinning mechanism of gradient colored yarn was established based on three-channel CNC spinning mechanism and the time-series yarn simulation model, and the time-series spinning processing parameters of three-channel CNC spinning machine were devised. Four gradient colored yarns with different gradient paths were designed and prepared, the linear density, twist, unevenness, surface hairiness, and tensile strength of the spun yarns were measured, compared, and analyzed, and knitted fabrics with color gradient effect were fabricated by small circular knitting machine to obtain continuous and natural color transition with a dreamy and mysterious color effect.

Effects of Contact Area and Contact Shape on Nonlinear Fluid Flow Properties of Fractures by Solving Navier-Stokes Equations

Abstract The influences of contact shape and contact area on nonlinear fluid flow properties through fractures are investigated by solving Navier-Stokes equations. The evolutions of nonlinear relationships between flow rate and hydraulic pressure drop, Forchheimer coefficients, nonlinear factor, critical hydraulic gradient, distributions of flow streamlines, and tracer flow paths at different times are systematically estimated. The results show that the nonlinear relationships between flow rate and hydraulic pressure drop can be well described by Forchheimer’s law, in which the nonlinear term coefficient b is approximately three orders of magnitude larger than the linear term coefficient a. The smaller contact area corresponds to smaller variations in many aspects such as flow rate, critical hydraulic gradient, flow streamlines, and tracer flow paths. The critical hydraulic gradient decreases with the increasing degree of contact shape variations while the contacts have the same mean area. The increase in hydraulic pressure drop can induce significant eddies and decrease the permeability and/or conductivity of fractures. However, the distributions of streamlines and tracer flow paths are not dramatically disturbed under a large hydraulic pressure drop.

Electrostatic Potential Topology for Probing Molecular Structure, Bonding and Reactivity.

Following the pioneering investigations of Bader on the topology of molecular electron density, the topology analysis of its sister field viz. molecular electrostatic potential (MESP) was taken up by the authors’ groups. Through these studies, MESP topology emerged as a powerful tool for exploring molecular bonding and reactivity patterns. The MESP topology features are mapped in terms of its critical points (CPs), such as bond critical points (BCPs), while the minima identify electron-rich locations, such as lone pairs and π-bonds. The gradient paths of MESP vividly bring out the atoms-in-molecule picture of neutral molecules and anions. The MESP-based characterization of a molecule in terms of electron-rich and -deficient regions provides a robust prediction about its interaction with other molecules. This leads to a clear picture of molecular aggregation, hydrogen bonding, lone pair–π interactions, π-conjugation, aromaticity and reaction mechanisms. This review summarizes the contributions of the authors’ groups over the last three decades and those of the other active groups towards understanding chemical bonding, molecular recognition, and reactivity through topology analysis of MESP.

Computational Design of Compositionally Graded Alloys for Property Monotonicity

Abstract Functionally graded materials (FGMs) exhibit spatial gradients in properties that can be exploited to satisfy multiple conflicting performance objectives in the same part. Compositionally graded alloys are a subclass of FGMs that have received increased attention with the development of metal additive manufacturing. However, the formation of secondary phases can often lead to cracks or deleterious properties in these materials. In prior work, a computational methodology was presented that can design compositional gradients to avoid these phases at any temperature without the need to visualize phase diagrams (Kirk et al., 2018, “Computational Design of Gradient Paths in Additively Manufactured Functionally Graded Materials,” ASME J. Mech. Des., 140(11), p. 111410). The methodology optimizes gradient paths through composition space for a specified cost function, but prior work only considered minimizing path length or maximizing the distance from undesirable phases. In this work, a new cost function is presented to produce compositional paths with optimal property gradients. Specifically, monotonicity is presented as the optimal quality of a pathwise property gradient because monotonic property gradients can be transformed to nearly any form on the part by controlling deposition rate. The proposed cost function uses a metric for non-monotonicity to find the shortest path with monotonic properties and is shown to be compatible with optimal path planners. A synthetic case study examines the effect of a cost function parameter on the trade-off between length and monotonicity. The cost function is also demonstrated in the Fe-Co-Cr system to find a compositional path with monotonic gradients in coefficient of thermal expansion (CTE). The deposition of the path on a hypothetical part is then planned subject to a maximum deposition rate and CTE gradient. Future work is proposed to extend the framework to optimize multiple properties at once and to incorporate multi-material topology optimization (MMTO) techniques into a complete design methodology for functionally graded metal parts.

Stream and floodplain restoration impacts riparian zone hydrology of agricultural streams.

While the influence of stream restoration on vertical and lateral hyporheic exchange has been documented, impacts on broader riparian zone hydrology have not been thoroughly investigated. We quantified riparian water table dynamics, hydraulic gradient, and groundwater flow paths and fluxes across a range of hydrologic conditions following natural channel design restoration (riparian regrading, in-channel cross-vane structure installation). Water table measurements were collected at least once per season for 2.5 years to capture water levels during baseflow conditions from networks of wells and piezometers at sites with different stream morphology (created riffle-cross-vane-scour pool complexes versus natural pools and riffles), restoration status (agricultural restored, unrestored, forested reference), and riparian characteristics (slope, soils, topography) in North Carolina, USA. The regraded riparian zone had higher near-stream water tables (< 0.5 m below ground surface) than the unrestored site. Riffle-cross-vane complexes induced a zone of low hydraulic gradient that spanned 30-40% of the riparian area, similar to groundwater dynamics near beaver dams. This effect persisted regardless of hydrologic condition (wet or dry) or scour pool status (functioning or filled-in). Riffles also promoted a lower near-stream hydraulic gradient at the forested site. Conversely, the influence of stream features on riparian groundwater was minimal at the unrestored site, where groundwater fluxes were controlled by hillslope inputs and riparian geomorphology. Overall, restoration enhanced stream-riparian zone hydrologic interaction beyond the immediate hyporheic zone. Our work stresses that cross-vanes, even when partially buried by sediments post-restoration, impact whole floodplain hydrology in a more significant way than shown by prior stream restoration studies.

Well-Posed Geoscientific Visualization Through Interactive Color Mapping

Scientific visualization aims to present numerical values, or categorical information, in a way that enables the researcher to make an inference that furthers knowledge. Well-posed visualizations need to consider the characteristics of the data, the display environment, and human capacity. In the geosciences, visualizations are commonly applied to spatially varying continuous information or results. In this contribution we make use of a suite of newly written computer applications which enable spatially varying data to be displayed in a performant graphics environment. We present a comparison of color-mapping using illustrative color spaces (RGB, CIELAB). The interactive applications display the gradient paths through the chosen color spaces. This facilitates the creation of color-maps that accommodate the non-uniformity of human color perception, producing an image where genuine features are seen. We also take account of aspects of a dataset such as parameter uncertainty. For an illustrative case study using a seismic tomography result, we find that the use of RGB color-mapping can introduce non-linearities in the visualization, potentially leading to incorrect inference. Interpolation in CIELAB color space enables the creation of perceptually uniform linear gradients that match the underlying data, along with a simply computable metric for color difference, ΔE. This color space assists accuracy and reproducibility of visualization results. Well-posed scientific visualization requires both visual literacy and visual numeracy on an equal footing with clearly written text. It is anticipated that this current work, with the included color-maps and software, will lead to wider usage of informed color-mapping in the geosciences.

Functionally Graded Materials through robotics-inspired path planning

Functional grading has recently seen renewed interest with the advancement of additive manufacturing. Unfortunately, the integrity of functional gradients in alloys tends to be compromised by the presence of brittle phases. Recently, CALPHAD-based tools have been used to generate isothermal phase diagrams that are in turn utilized to plan gradient paths that avoid these phases. However, existing frameworks rely extensively on the (limited) ability of humans to visualize and navigate high-dimensional spaces. To tackle this challenge, a Machine Learning approach was used here to map out undesirable regions as ‘obstacles’, while a path-planning algorithm, commonly used in robotics community, was utilized to identify a path in a composition space that would avoid the obstacles, while simultaneously minimizing a cost function. This framework was validated by designing and 3-D printing a functional gradient in bulk samples from 316L stainless steel to pure chromium with a multi-material direct laser deposition system. Both the planned gradient and simple linear gradient samples were fabricated and characterized in as-deposited and heat-treated states to determine local compositions, microstructure and phase constituents. The planned gradient resulted in complete elimination of the detrimental σ phase after heat treatment, demonstrating the success of the methodology.