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Explicit Formulations of Widely-Used Wall Models for Large-Eddy Simulation
No full textython implementation of the algorithms for deriving explicit wall models for LES. Accompanies associated paper in the Physics of Fluids journal. Also contains files with exact coefficient values derived for the models, and scripts to reproduce the most important figures. See README.md for more details, and https://github.com/aanslab-opensource/explicit-wall-models for latest updates
Spatial patterns in reef fish biomass and trait diversity along a natural environmental gradient
No full textMeasure-based approach to mesoscopic modeling of optimal transportation networks
No full textWe propose a mesoscopic modeling framework for optimal transportation networks with biological applications. The network is described in terms of a joint probability measure on the phase space of tensor-valued conductivity and position in physical space. The energy expenditure of the network is given by a functional consisting of a pumping (kinetic) and metabolic power-law term, constrained by a Poisson equation accounting for local mass conservation. We establish convexity and lower semicontinuity of the functional on appropriate sets. We then derive its gradient flow with respect to the 2-Wasserstein topology on the space of probability measures, which leads to a transport equation, coupled to the Poisson equation. To lessen the mathematical complexity of the problem, we derive a reduced Wasserstein gradient flow, taken with respect to the tensor-valued conductivity variable only. We then construct equilibrium measures of the resulting PDE system. Finally, we derive the gradient flow of the constrained energy functional with respect to the Fisher–Rao (or Hellinger–Kakutani) metric, which gives a reaction-type PDE. We calculate its equilibrium states, represented by measures concentrated on a hypersurface in the phase space
A pan-Arctic perspective on the influence of ice algae on sea-ice nutrient concentrations
No full textSea-ice algae account for a substantial part of annual primary production in ice-covered waters and are an important component of the Arctic marine food web. With climate-induced changes to snow and sea-ice cover and their impact on the surface ocean, such as earlier melt, thinner ice, and increased upper-ocean stratification, a shift toward earlier and more extensive nutrient limitation on ice algal growth can be expected. Therefore, increasing our understanding of the processes governing nutrient supply and uptake by sea-ice algae is essential. Here, we compiled a pan-Arctic dataset of concentrations of sea-ice and sub-ice nutrients and sea-ice chlorophyll a (chl a) to assess their regional and seasonal variability, as well as the relationship of sea-ice algae and nutrient dynamics in the Arctic Ocean. This dataset indicates that bottom sea-ice nutrient and chl a concentrations were highest in the central Canadian Arctic Archipelago (Resolute Passage) due to tidal-driven mixing at the ocean-ice interface, and lowest in the Arctic Ocean basins. At the regional scale, Pacific and Atlantic Water influence variability in sea-ice and sub-ice nutrient concentrations. Significant positive relationships of bottom sea-ice nutrient versus chl a concentrations were ubiquitous across the Arctic during the ice algal bloom, suggesting intracellular nutrient storage as an important mechanism to support ice algal growth. This relationship in turn alters nutrient ratios within the sea ice relative to sub-ice waters, decreasing NOx:PO4 ratios, while increasing NOx:Si(OH)4 ratios. In contrast, bottom sea-ice nutrient-chl a relationships were less common and sometimes negative when nutrient concentrations were low, likely reflecting nutrient limitation. In conclusion, we have demonstrated a pan-Arctic, yet regionally specific, influence of the ice algal community on bottom sea-ice nutrient concentrations.We would like to thank those who contributed by sharing their published and unpublished data, special thanks to Drs. Dorte H. Søgaard, MA van Leeuwe, Laurent Oziel, Miriam Marquardt, Ana Vader, Kwanwoo Kim, and Laura Dalman. We also thank Dr. Kristina Brown for providing fruitful feedback on the manuscript.
FA was supported by Northern Scientific Training Program (NSTP), University of Manitoba Graduate Fellowship (UMGF), University of Manitoba-GETS fund. CJM was supported by NSERC Discovery Grant and Norther Research Supplement. EL was supported by EU’s Horizon Europe research and innovation programme under grant agreement No 101136875 and by the Fram Centre program SUDARCO, Cristin ID 2551323. PA was supported by the Research Council of Norway (project no. 244646) and the Centre for ice, Cryosphere, Carbon and Climate (iC3) supported by the Research Council of Norway through its Centres of Excellence funding scheme (project no. 332635). MC, AF, EMJ, PA, RG, and LMO were supported by the Research Council of Norway through the project The Nansen Legacy (RCN #276730). KC, RDM, RG, and PA were supported by the Research Council of Norway through the project BREATHE (Bottom-sea ice Respiration and nutrient Exchanges Assessed for THE Arctic) (RCN # 325405). The work by KC is also supported by ERC StG Micro-SHIFT (Microbial life of Sea ice Habitats Investigated for The Arctic, grant #101162830) projects. LW, MO and sea ice macronutrient data from Utqiaġvik, AK (Whitmore et al., 2024) and the MOSAiC campaign (Oggier et al., 2024; Salganik et al., 2024) were supported by the National Science Foundation Office of Polar Programs (OPP-1735862). Collection and analysis of data from near Utqiaġvik, AK provided by ARJ was partly supported through grants from the US National Science Foundation (ARC-0221055, ARC-0454955, ARC-0454726, and ARC10-23348). STV work (including nutrient measurements) during the MOSAiC campaign was funded by the German Federal Ministry for Education and Research (BMBF) through financing the Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung (AWI) and the Polarstern expedition PS122 under grant N-2014-H-060_Dethloff and by AWI through project AWI_ECO. STV received additional funding for nutrient observations through the project Primary productivity driven by Escalating Arctic NUTrient fluxeS (PEANUTS) grant #03F0804A. AN was supported by funding from NSERC and Polar Continental Shelf Program (PCSP) of Natural Resources Canada. SHL is supported by KIMST RS-2021-KS211500 and RS-2025-00555422. MG and EAF were supported through grants from the Canadian IPY Federal Program office and NSERC of Canada. This work represents a contribution to the Biogeochemical exchange processes at sea ice interfaces (BEPSII) network and the Canada Excellence Research Chair unit at the University of Manitoba
Applying multi-criteria decision analysis in the assessment of carbon dioxide removal technologies for Saudi Arabia
No full textThis paper investigates the potential application of various carbon dioxide removal strategies within Saudi Arabia. It evaluates both traditional and innovative carbon dioxide removal solutions using a multi-criteria decision analysis approach. The study defines performance, economic, and environmental criteria, including factors such as technology readiness, CO2 permanence, costs, environmental impacts, and socio-economic co-benefits. Furthermore, it examines the policy and regulatory environment, as well as the feasibility and preparedness for monitoring, reporting, verification, and certification of different carbon dioxide removals. The analysis identifies five key groups of carbon dioxide removals for prioritization in Saudi Arabia: (i) energy-from-waste and biomethane production integrated with carbon capture, utilization, and storage, (ii) direct air capture, (iii) biomass pyrolysis producing biochar, (iv) conventional nature-based solutions, and (v) enhanced weathering. The study suggests that policies in the Kingdom should focus on diverting waste from landfills to 'energy-from-waste with carbon capture, utilization, and storage' facilities and on utilizing waste heat from industrial sites for direct air capture networks. By offering this detailed analysis, the paper seeks to provide valuable guidance for policymakers, researchers, and stakeholders in advancing carbon dioxide removal efforts in Saudi Arabia
Jafet Belmont, Sara Martino, Janine Illian and Rue Håvard“s contribution to the Discussion of the 'Discussion Meeting on the Analysis of citizen science data”
No full textWe suggest considering integrated-nested Laplace approximation (INLA) as an alternative, more
efficient fitting approach that can be used in the current context since occupancy models can now
be fitted using the library R-INLA (Belmont et al., 2024), provided detection depends on fixed effects
only. To illustrate this, we have used INLA to fit the model discussed in the paper to the Ringlet
butterfly dataset (R code accessible through https://github.com/Ecol-Stats/CS_data_analysis).
This took about 30 seconds on a standard Apple M2 Pro machine, i.e. about 120 times faster than the VI approach with very similar estimates, see Figures 1a and 1
Impacts of water conservation, wastewater treatment, and reuse on water quantity and quality stress mitigation in China
No full textWastewater treatment plays a crucial role in removing pollutants. Water conservation and reuse of wastewater help to reduce freshwater use and to alleviate water stress. However, the extent to which water conservation, wastewater treatment, and reuse can contribute to water stress mitigation is not clear. This study aims to investigate the impact of water conservation, wastewater treatment, and reuse on both water quantity and quality stress mitigation in China. The investigation is based on a dataset mapping water quantity and pollutant flows across 32 sectors in 31 provinces in 2017 and a dataset of 7411 wastewater treatment plants containing information on wastewater quantity and quality. The findings show that wastewater reuse can reduce provincial water quantity stress by less than 10% and alleviate water stress in 4 out of 25 water-stressed provinces. In contrast, water conservation can contribute to water quantity stress reduction by 31% on average. When water conservation measures and reuse are jointly implemented, quantity stress levels can significantly be alleviated in 19 out of 25 water-stressed provinces, with quantity stress reductions ranging from 25% to 74%. The contribution of wastewater treatment to water quality stress mitigation varies between 6% and 86%, with an average of 29%. Nevertheless, wastewater treatment cannot sufficiently safeguard most regions against water quality stress. This is evident as 25 out of 29 water quality-stressed provinces continue to suffer from quality stress despite implementing wastewater treatment and water conservation practices. Additional measures such as non-point-source pollution control should be implemented alongside wastewater treatment to eliminate provincial quality stress.Dan Wang was funded by the China Scholarship Council (No. 201908440332) and Sustainable Society PhD Grant at University of Groningen. Dan Wang would like to thank the International Institute for Applied Systems Analysis (IIASA), as this work was partly conducted during the participation at the IIASA's Young Scientist Summer Programme (YSSP). Wei Zhang thanks the support from Jiangsu Province Carbon Peak and Carbon Neutral Key R&D Programme (No. BE2022861). Weili Ye thanks Joint Study on Ecological Protection and High-Quality Development in the Yellow River Basin (Phase I)(2022-YRUC-01-0603-05)and Major Science and Technology Program for Water Pollution Control and Treatment of China (No.2018ZX07111001). Guangxue Wu was funded by Galway University Foundation
Metal Organic Frameworks as Photocatalysts in Water Splitting for Hydrogen Evolution
No full textMetal-organic frameworks (MOFs) are organic-inorganic hybrid materials
which are used and actively explored in the photocatalytic process of water splitting
for green hydrogen production thanks to their appealing features such as high surface
area, tunable pore sizes, excellent thermal and chemical stability as well as structural
flexibility. However, they do exhibit certain limitations such as scalability, inefficient
photocatalytic activity, and stability increasing research interest to optimize effective
photocatalysts. This work proposes a novel hybrid photocatalyst, where Ni₂P
nanoclusters, showing high hydrogen evolution reaction (HER) activity, are
incorporated into the porous framework of NH₂-MIL-125(Ti), with tunable band
structure and visible-light absorption, through an in situ phosphidation strategy. This
is expected to be a cost-effective alternative for expensive noble metals, that shows
enhanced charge separation and improved stability through nanoparticles
confinement. The proposed approach highlights the potential of MOF–cocatalyst
hybrids in advancing the development of clean and renewable hydrogen energy
solutions.We acknowledge KAUST Academy and Prof. Yun Hau Ng for supporting this work
Gabor CNN-based improvement of tunnel seismic migration imaging and field application with domain adaptation assistance
No full textIn tunnel seismic forward-prospecting, the accuracy of migration imaging impacts the geological interpretation of the area ahead of the tunnel face. However, the traditional reverse time migration (RTM) method, which is the adjoint of the Born forward modeling, often yields approximate estimations of reflectivity. This approximation error becomes even more pronounced in the context of small offset tunnel conditions. To address this issue, we propose a novel method for enhancing tunnel RTM imaging by leveraging Gabor Convolutional Neural Networks (CNN). In our approach, we employ a Gabor CNN that incorporates learnable parameters within the Gabor filters to extract pertinent features from tunnel RTM imaging results. By training the network with RTM images as input and the true reflectivity as labels, we enable the network to learn underlying patterns and improve the quality of the imaging. Notably, we tackle the challenge of limited labeled field data by introducing MLReal, a domain adaptation method. MLReal enhances the generalizability of the proposed network to field data by employing an inter-processing and transformation approach that aligns the target data with the synthetic dataset. This alignment allows the network to adapt to real-world field conditions, bridging the gap between synthetic training data and field applications. Extensive numerical experiments validated the superiority of the Gabor CNN, showcasing its ability to generate results closely resembling true reflectivity while outperforming LSRTM. Furthermore, a field case study is conducted in a water transmission tunnel as a practical application to verify the potential of the MLReal-assisted Gabor CNN.The authors would like to sincerely thank the anonymous reviewers for their insightful comments and suggestions. This work was supported in part by the National Natural Science Foundation of China (No. 52409134), the China Postdoctoral Science Foundation (No. GZB20240407), and Young Talent of Lifting engineering for Science and Technology in Shandong, China (No. SDAST2024QTB012). (Corresponding author: Senlin Yang.) The authors also thank KAUST for its support and the seismic wave analysis group (SWAG) for constructive discussions. We are also grateful to Oleg Ovcharenko and colleagues for their MLReal provided on GitHub (https://github.com/swag-kaust/dda_pytorch)
AI-Driven Resilient Fault Diagnosis of Bearings in Rotating Machinery
No full textPredictive maintenance is increasingly important in rotating machinery to prevent unexpected failures, reduce downtime, and improve operational efficiency. This study compares the efficacy of traditional machine learning (ML) and deep learning (DL) techniques in diagnosing bearing faults under varying load and speed conditions. Two classification tasks were conducted: a simpler three-class task that distinguishes healthy bearings, inner race faults, and outer race faults, and a more complex nine-class task that includes faults of varying severity in the inner and outer races. In this study, the machine learning algorithm ensemble bagged trees, achieved maximum accuracies of 93.04% for the three-class and 87.13% for the nine-class classifications, followed by neural network, SVM, KNN, decision tree, and other algorithms. For deep learning, the CNN model, trained on scalograms (time–frequency images generated by continuous wavelet transform), demonstrated superior performance, reaching up to 100% accuracy in both classification tasks after six training epochs for the nine-class classifications. While CNNs take longer training time, their superior accuracy and capability to automatically extract complex features make the investment worthwhile. Consequently, the results demonstrate that the CNN model trained on CWT-based scalogram images achieved remarkably high classification accuracy, confirming that deep learning methods can outperform traditional ML algorithms in handling complex, non-linear, and dynamic diagnostic scenarios.The authors acknowledge the help and support provided by the Faculty of Mechanical Engineering of GIK Institute, Pakistan, in facilitating the experimental setup and providing the necessary resources for this research. We thank Ghulam Ishaq Khan Institute (GIKI) for their constant support and help.
This research was supported by the Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI). The Article Processing Charge (APC) was generously covered by Asif Khan and Ghulam Jawad Sirewal. The authors gratefully acknowledge their support