Effects of injection well operation conditions on CO 2 storage capacity in deep saline aquifers
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Energy & Fuels↗
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Effects of Slope Aspect and Rainfall on Belowground Deep Fine Root Traits and Aboveground Tree Height
The vertical root distribution and rooting depth are the main belowground plant functional traits used to indicate drought resistance in arid and semiarid regions. The effects of the slope aspect on the aboveground traits are visible but not the belowground deep root traits. We aimed to investigate the fine root traits of the locust tree ( Robinia pseudoacacia L.) planted on southerly and northerly aspects, and the variations in the rooting depth in regions with different rainfall, as well as assessing how deep rooting, might affect the response to drought in a loess region. We selected three study sites with different rainfall amounts, with six sampling plots at each site (three each with southerly and northerly aspects). Soil core samples were collected down to the depth where no roots were present. The locust trees tended to develop deeper fine roots rather than greater heights. The tree height and diameter were greater for locust trees on northerly aspects, whereas trees on southerly aspects had significantly deeper rooting depths. Fine root traits (root length, root area, and root dry weight density) were higher in the southerly aspect for both Changwu and Ansai, but lower in Suide. The ratio of the root front depth tree height ranged from 1.04 to 3.17, which was higher on southerly than northerly aspects, and it increased as the rainfall decreased. Locust tree growth traits (belowground fine root and aboveground tree height) were positively correlated with the mean annual rainfall. The soil moisture content of the topsoil decreased as the rainfall decreased, but the pattern varied in the deep layer. Our results suggest that the variations in the belowground rooting depth under different slope aspects may be related to plant survival strategies. The vertical extension of the rooting depth and tree height may be key functional traits that determine plant growth in drought-prone regions.
Lihui, Ma↗
Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary
Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.
60 APPLIED LIFE SCIENCES↗
Well Logs: A Non-Invasive Approach for Elucidating the Spatial Distribution of In-Situ Stress in Deep Subsurface Geologic Formations Considered for CO2 Storage
Routine and geomechanical well logs for 3 wells— State Otsego Lake 8-15A, Lawnichak 9-33, Chester 8-16.
anisotropy↗
Mind-the-gap - Part III: Doppler velocity measurements from space
Convective motions and hydrometeors microphysical properties are highly sought-after parameters for evaluating atmospheric numerical models. With most of the Earth's surface covered by water, space-borne Doppler radars are ideal for acquiring such measurements at a global scale. While these systems have proven to be useful tools for retrieving cloud microphysical and dynamical properties from the ground, their adequacy, and specific requirements for spaceborne operation still need to be evaluated. Comprehensive forward simulations enable us to assess the advantages and drawbacks of six different Doppler radar architectures currently planned or under consideration by space agencies for the study of cloud dynamics. Radar performance is examined against state-of-the-art numerical model simulations of well-characterized shallow and deep, continental, and oceanic convective cases. Mean Doppler velocity (MDV) measurements collected at multiple frequencies (13, 35 and 94 GHz) provide complementary information in deep convective cloud systems. The high penetration capability of the 13-GHz radar enables to obtain a complete, albeit horizontally under-sampled, view of deep convective storms. The smaller instantaneous field of view (IFOV) of the 35-GHz radar captures more precise information about the location and size of convective updrafts above 5-8 km height of most systems which was determined is the portion of storms where the mass flux peak is typically located. Finally, the lower mean Doppler velocity uncertainty of displaced phase center antenna (DPCA) radars makes them an ideal system for studying microphysics in shallow convection and frontal systems, as well as ice and mixed-phase clouds. It is demonstrated that a 94-GHz DCPA system can achieve retrieval errors as low as 0.05-0.15 mm for raindrop volume-weighted mean diameter and 25% for rime fraction (for a -10 dBZ echo).
54 ENVIRONMENTAL SCIENCES↗
Proxy-Based Bayesian Inversion Of Poroelastic Simulations To Interpret Strain Tensor Data Measured During Well Testing
The long runtimes of 3D poroelastic numerical simulators makes it impractical to interpret deformation datasets using many inversion schemes. Recent advances in instrumentation have made it possible to measure the strain tensor during well testing, but the lack of robust inversion methods is limiting the ability to interpret these data. We have developed an inversion workflow that reduces the number of computations required to complete a Bayesian inversion using DREAMzs. The workflow trains a KNN model using output from the poroelastic simulator, and then uses the KNN model as a proxy for the simulator during inversion. The workflow also includes a strategy for ensuring the results from the proxy model converge to the results from the simulator, ensuring the accuracy of the final results. An idealized example configured to represent a well test in a deep aquifer is used to verify that the workflow correctly identifies parameters and characterizes noise. Field data measured using strainmeters during an injection test at an oil reservoir in Oklahoma are used to evaluate performance with a real dataset. The workflow identified 265 history matching solutions out of 1240 total simulation runs (21% acceptance ratio), and the results are used to characterize posterior parameter distribution and evaluate the prediction uncertainty. This approach makes it feasible to invert strain data measured during well testing and this has the potential to improve the characterization of aquifers and reservoirs.
Roudini, Soheil↗
hippynn Python Package
hippynn is a python package for defining, training, and applying neural networks to atomistic systems. In particular, it focuses on Hierarchical Interacting Particle Neural Networks (HIP-NNs), a deep learning architecture for atomistic systems. HIP-NNs take input data describing the properties of atomistic systems (often generated using ab-initio quantum mechanics) to learn fast and accurate models. A trained HIP-NN can predict potential energy surfaces, atomic charges, and more. hippynn uses PyTorch for portable high-performance code, including both CPU and GPU support. hippynn allows for extensive customization, including user-defined models and loss functions, to facilitate future research into extensions of HIP-NN as well as other atomistic deep learning models.
Lubbers, Nicolas↗
Aerosol-deep convection interaction based on joint cell-thermal tracking in Large Eddy Simulations during the TRACER campaign
In cumulus clouds, aerosol concentrations control cloud droplet concentrations, modifying cloud radiative properties, precipitation processes, and cloud electrification. However, mechanisms of aerosol-deep convection interactions are not well understood due to complex cloud dynamics and microphysics. We investigate the interaction of aerosols with isolated deep convection using Large Eddy Simulations of two cases during the TRacking Aerosol Convection interactions ExpeRiment (TRACER) near Houston, Texas, using a joint cell-thermal tracking algorithm. Cumulus thermals are droplet generators, since supersaturation and droplet nucleation coincide with thermal centers, where the strongest updrafts occur. Primary ice crystal formation does not take place inside thermals, but at layers where previous thermals detrained moisture. As subsequent thermals containing supercooled droplets penetrate these layers, hail and graupel form at or near these thermals. Higher aerosol concentrations result in higher droplet concentrations that suppress drizzle, delay warm rain processes, and transport more moisture aloft. This increases snow and ice amount, as well as graupel and hail, leading to more lightning. Polluted thermals initiate at slightly higher altitudes, and are slightly larger and faster, suggesting a weak invigoration. We also find more thermals per cell, but fewer isolated cells, since convection is more aggregated and intense, especially near the end of the 24 h simulation. Non-linear mesoscale feedback likely triggered by temperature and moisture responses to aerosol-thermal interactions causes the aggregation. Time-lagged aerosol-reinitialization experiments show that the mesoscale response is the predominant forcing for the invigoration. These changes happen within one day, on a smaller scale than previously suggested.
54 ENVIRONMENTAL SCIENCES↗
Super-resolution and segmentation deep learning for breast cancer histopathology image analysis
Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.
60 APPLIED LIFE SCIENCES↗
A fragmentation approach to jet flavor
An intuitive definition of the partonic flavor of a jet in quantum chromodynamics is often only well-defined in the deep ultraviolet, where the strong force becomes a free theory and a jet consists of a single parton. However, measurements are performed in the infrared, where a jet consists of numerous particles and requires an algorithmic procedure to define their phase space boundaries. To connect these two regimes, we introduce a novel and simple partonic jet flavor definition in the infrared. We define the jet flavor to be the net flavor of the partons that lie exactly along the direction of the Winner-Take-All recombination scheme axis of the jet, which is safe to all orders under emissions of soft particles, but is not collinear safe. Collinear divergences can be absorbed into a perturbative fragmentation function that describes the evolution of the jet flavor from the ultraviolet to the infrared. The evolution equations are linear and a small modification to traditional DGLAP and we solve them to leading-logarithmic accuracy. The evolution equations exhibit fixed points in the deep infrared, we demonstrate quantitative agreement with parton shower simulations, and we present various infrared and collinear safe observables that are sensitive to this flavor definition.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Optoelectronic property comparison for isostructural Cu 2 BaGeSe 4 and Cu 2 BaSnS 4 solar absorbers
To target mitigation of anti-site defect formation in Cu 2 ZnSnS 4–x Se x , a new class of chalcogenides, for which Ba or Sr (group 2) replace Zn (group 12), has recently been introduced for prospective solar absorber application. Cu 2 BaGeSe 4 (CBGSe) and Cu 2 BaSnS 4 (CBTS) are two such compounds, which share a common trigonal crystal structure (P3 1 space group) and similar quasi-direct band gap (~2 eV). While CBTS-based films have already been studied, there are no reports yet on films and solar cells based on related CBGSe. To identify key differences and similarities in the electronic properties between these two materials, electronic characteristics (e.g., carrier concentration, mobility, electron affinity, defect levels, recombination, and charge carrier kinetics) of vacuum-deposited CBGSe and CBTS films are compared using a variety of characterization methods. Hall effect measurements reveal that CBGSe films have relatively higher hole carrier concentration and lower mobility (3 × 10 15 cm –3 , 0.6 cm 2 V –1 s –1 ) compared to CBTS (5 × 10 12 cm –3 , 3.5 cm 2 V –1 s –1 ). Photoelectron spectroscopy yields low electron affinity values for both CBGSe (3.7 eV) and CBTS (3.3 eV), pointing to the necessity of pursuing low electron affinity buffer materials for both types of absorbers. At low temperatures, CBGSe films show free-exciton photoluminescence, as well as pronounced deep-level emission at ~1.4 eV, while CBTS films exhibit a strong bound-exciton signal with noticeably less intense deep-level emission than for CBGSe. Charge carrier kinetics, transport, and recombination properties of both types of films are also analyzed using optical-pump terahertz-probe spectroscopy and time-resolved microwave conductivity. The first CBGSe prototype solar cells (using chemical bath deposited CdS as a buffer layer) show a maximum of 1.5% efficiency with ~0.62 V open-circuit voltage. Furthermore, the measured properties point to possible limiting factors for CBGSe and related films for PV and optoelectronics and provide insights on possible approaches for improvement within this multinary chalcogenide family.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Computational Fermi Level Engineering and Doping-Type Conversion of Mg:Ga 2 O 3 via Three-Step Synthesis Process
Gallium oxide (Ga 2 O 3 ) is being actively explored for electronics that can operate at high power, temperature, and frequency as well as for deep-ultraviolet optoelectronics and other applications due to its ultra-wide bandgap (UWBG) and low projected fabrication cost of large-size and high-quality crystals. Efficient n-type doping of monoclinic beta-phase of Ga 2 O 3 has been achieved, but p-type doping faces fundamental obstacles due to compensation, deep acceptor levels, and the polaron transport mechanism of free holes. However, aside from the challenges of achieving p-type conductivity, plenty of opportunity exists to engineer the position of the Fermi level for improved design of Ga 2 O 3 -based devices. We use first-principles defect theory and defect equilibrium calculations to simulate a three-step growth-annealing-quench synthesis protocol for hydrogen-assisted Mg doping in ß-Ga 2 O 3 . The simulations take into account the gas phase equilibrium between H 2 , O 2 , and H 2 O, which determines the H chemical potential. We predict Ga 2 O 3 doping-type conversion to a net p-type regime after growth under reducing conditions in the presence of H2 followed by O-rich annealing, which is a similar process to Mg acceptor activation by H removal in GaN. For equilibrium annealing with re-equilibration of compensating O vacancies, there is an optimal temperature that maximizes the Ga 2 O 3 net acceptor density for a given Mg doping level; the acceptor density is further increased in the non-equilibrium annealing scenario without re-equilibration. After quenching to operating temperature, the Ga 2 O 3 Fermi level drops below mid-gap down to about 1.5 eV above the valence band maximum, creating a significant number of uncompensated neutral Mg Ga 0 acceptors. The resulting free hole concentration in Ga 2 O 3 is very low even at elevated operating temperature (~10 8 cm -3 at 400°C) due to the deep energy level of these Mg acceptors, and hole conductivity is further impeded by the polaron hopping mechanism. However, the Fermi-level reduction and suppression of free electron density in this doping-type converted (NA>ND) Ga 2 O 3 material are important for improved designs of Ga 2 O 3 electronic devices. These results illustrate the power of computational predictions not only for new materials but also for their synthesis science.
36 MATERIALS SCIENCE↗
Observational window effects on multi-object reverberation mapping
ABSTRACT Contemporary reverberation mapping campaigns are employing wide-area photometric data and high-multiplex spectroscopy to efficiently monitor hundreds of active galactic nuclei (AGNs). However, the interaction of the window function(s) imposed by the observation cadence with the reverberation lag and AGN variability time-scales (intrinsic to each source over a range of luminosities) impact our ability to recover these fundamental physical properties. Time dilation effects due to the sample source redshift distribution introduce added complexity. We present comprehensive analysis of the implications of observational cadence, seasonal gaps, and campaign baseline duration (i.e. the survey window function) for reverberation lag recovery. We find that the presence of a significant seasonal gap dominates the efficacy of any given campaign strategy for lag recovery across the parameter space, particularly for those sources with observed-frame lags above 100 d. Using the Australian Dark Energy Survey as a baseline, we consider the implications of this analysis for the 4MOST/Time-Domain Extragalactic Survey campaign providing concurrent follow-up of the Legacy Survey of Space and Time deep-drilling fields, as well as upcoming programmes. We conclude that the success of such surveys will be critically limited by the seasonal visibility of some potential field choices, but show significant improvement from extending the baseline. Optimizing the sample selection to fit the window function will improve survey efficacy.
79 ASTRONOMY AND ASTROPHYSICS↗
Contrastive learning for robust representations of neutrino data
In neutrino physics, analyses often depend on large simulated datasets, making it essential for models to generalize effectively to real-world detector data. Contrastive learning, a well-established technique in deep learning, offers a promising solution to this challenge. By applying controlled data augmentations to simulated data, contrastive learning enables the extraction of robust and transferable features. This improves the ability of models trained on simulations to adapt to real experimental data distributions. In this paper, we investigate the application of contrastive learning methods in the context of neutrino physics. Through a combination of empirical evaluations and theoretical insights, we demonstrate how contrastive learning enhances model performance and adaptability. Additionally, we compare it to other domain adaptation techniques, highlighting the unique advantages of contrastive learning for this field. Published by the American Physical Society 2025
Wilkinson, Alex (ORCID:0000000253404506)↗
Local models for scatter estimation and descattering in polyenergetic X-ray tomography
We propose a new modeling approach for scatter estimation and descattering in polyenergetic X-ray computed tomography (CT) based on fitting models to local neighborhoods of a training set. X-ray CT is widely used in medical and industrial applications. X-ray scatter, if not accounted for during reconstruction, creates a loss of contrast in CT reconstructions and introduces severe artifacts including cupping, shading, and streaks. Even when these qualitative artifacts are not apparent, scatter can pose a major obstacle in obtaining quantitatively accurate reconstructions. Our approach to estimating scatter is, first, to generate a training set of 2D radiographs with and without scatter using particle transport simulation software. To estimate scatter for a new radiograph, we adaptively fit a scatter model to a small subset of the training data containing the radiographs most similar to it. We compared local and global (fit on full data sets) versions of several X-ray scatter models, including two from the recent literature, as well as a recent deep learning-based scatter model, in the context of descattering and quantitative density reconstruction of simulated, spherically symmetrical, single-material objects comprising shells of various densities. Our results show that, when applied locally, even simple models provide state-of-the-art descattering, reducing the error in density reconstruction due to scatter by more than half.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Maximizing the Proppant Carrying and Viscoelastic Properties of the Bakken Hypersaline-Produced Water with High-Viscosity Friction Reducers for Sustainable Applications
Summary The development and production of unconventional reservoirs, such as the Bakken Formation, have become a resolved mystery for operators in North America since the arrival and advancement of horizontal drilling and hydraulic fracturing technologies. As a result, unconventional reservoir assets became the central focus of the oil and gas industry at the state, national, and global levels. The produced water from these activities in the Bakken Formation have high salt contents (110,000–350,000 ppm) total dissolve solids (TDS) and can pose significant challenges to the environment if not treated. Deep injection into disposal wells is the routine method used to get rid of the Bakken produced water. However, there have been some concerns that unrestrained injections, in addition to polluting the groundwater, could potentially lead to seismic activities either at the time of injection or in the near future. To diminish the environmental impacts that may be associated with induced seismicity, including the reduction of the costs of water acquisition, the produced water can be treated and reused in the hydraulic fracturing processes. Also, the treated water could be used for irrigation purposes, for power generation, and coal mining operations. The issues of waste water and residual oil high in TDS are challenges yet to be effectively addressed despite preceding research and studies on advancing produced water technologies. The goal of this study is to explore all applicable ways by which the produced water from the Bakken Formation can serve as a replacement base fluid for use with polymers like the high-viscosity friction reducers (HVFRs) to create hydraulic fracturing fluids that can be stable with reservoir conditions and also be able to minimize environmental impacts and cost of operations. Experimental investigations using the high-salinity produced water from the Bakken Formation with HVFRs were carried out. The studies included a base case that served as a bench mark for comparing the effectiveness of the other scenarios. The results indicate that the Bakken hyper-saline produced water can withstand effect of heavy metals, salinity, hardness and remain stable through different shear rates (66–330 s−1) when treated with higher dosages [4–8 gal/1,000 gal (gpt)] of HVFRs. Filtration and dilution were the only methods used on the Bakken Formation produced wate for this research
Engineering↗
EPiC-GAN: Equivariant point cloud generation for particle jets
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.
Buhmann, Erik↗
Effects of Jacobian Matrix Regularization on the Detectability of Adversarial Samples
The well-known vulnerability of Deep Neural Networks to adversarial samples has led to a rapid cycle of increasingly sophisticated attack algorithms and proposed defenses. While most contemporary defenses have been shown to be vulnerable to carefully configured attacks, methods based on gradient regularization and out-of-distribution detection have attracted much interest recently by demonstrating higher resilience to a broad range of attack algorithms. However, no study has yet investigated the effect of combining these techniques. In this paper, we consider the effect of Jacobian matrix regularization on the detectability of adversarial samples on the CIFAR-10 image benchmark dataset. We find that regularization has a significant effect on detectability, and in some cases can make an undetectable attack on a baseline model detectable. In addition, we give evidence that regularization may mitigate the known weaknesses of detectors to high-confidence adversarial samples. The defenses we consider here are highly generalizable, and we believe they will be useful for further investigations to transfer machine learning robustness to other data domains.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗