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High-quality control of receiver functions using a capsule neural network
No full textThe Red Sea-Gulf of Suez-Cairo-Alexandria Clysmic-Trend in northern Egypt is the main earthquake zone in the country, with a moderate-to-high seismic hazard and a history of significant earthquakes caused by rifting and active faulting. To improve our understanding of the tectonic and seismic processes in this area, more comprehensive imaging of the crustal structure is required. This can be achieved by increasing the number of receiver functions (RFs) recorded by the seismic stations in northern Egypt and the southeastern Mediterranean. Data handling and processing should also be automated to increase process efficiency. In this study, we developed a capsule neural network for automated selection of RFs. The model was trained on a dataset containing RFs (both selected and unselected) from five broadband stations in northern Egypt. Stations SLM, SIWA, KOT, NBNS, and NKL are located in the unstable shelf region of Egypt, where limited knowledge of the deep crustal structure is available. The proposed capsule neural network achieved an average precision of 80% on the test set. The automated selection of RFs using a capsule neural network has the potential to significantly improve the efficiency and accuracy of RF analysis, as demonstrated by the stacking test. This could lead to a better understanding of crustal structure and tectonic processes in northern Egypt and the southeastern Mediterranean.We are deeply thankful to our institution, NRIAG, for its essential support and resources, which contributed significantly to our research. Institutional occasions for scientific engagement have increased professional development. We would also like to thank the Egyptian National Seismic Network (ENSN) for providing the required data for this work and extend our thanks to Dr. Mahmoud Salam and Dr. Amr El Sharkawy for their individual assistance. The code corresponding to this study is available at https://github.com/omarmohamed15/RF-Capsule
Unprecedented continental drying, shrinking freshwater availability, and increasing land contributions to sea level rise.
No full textChanges in terrestrial water storage (TWS) are a critical indicator of freshwater availability. We use NASA GRACE/GRACE-FO data to show that the continents have undergone unprecedented TWS loss since 2002. Areas experiencing drying increased by twice the size of California annually, creating "mega-drying" regions across the Northern Hemisphere. While most of the world's dry/wet areas continue to get drier/wetter, dry areas are now drying faster than wet areas are wetting. Changes in TWS are driven by high-latitude water losses, intense Central American/European droughts, and groundwater depletion, which accounts for 68% of TWS loss over non-glaciated continental regions. "Continental drying" is having profound global impacts. Since 2002, 75% of the population lives in 101 countries that have been losing freshwater water. Furthermore, the continents now contribute more freshwater to sea level rise than the ice sheets, and drying regions now contribute more than land glaciers and ice caps. Urgent action is required to prepare for the major impacts of results presented.We wish to thank many colleagues who provided comments on this work
as it was in preparation. A portion of this work was conducted at the Jet Propulsion Laboratory,
California Institute of Technology, under contract with NASA. This publication serves as a
background paper for the World Bank’s forthcoming report on Global Water Monitoring. The
Global Water Monitoring report received generous funding provided through the Global Water
Security and Partnership (GWSP), a multi-donor trust fund administered by the World Bank’s
Water Global Practice and supported by Australia’s Department of Foreign Affairs and Trade,
Austria’s Federal Ministry of Finance, the Bill & Melinda Gates Foundation, Denmark’s Ministry
of Foreign Affairs, the Netherlands’ Ministry of Foreign Affairs, Spain’s Ministry of Economic
Affairs and Digital Transformation, the Swedish International Development Cooperation
Agency, Switzerland’s State Secretariat for Economic Affairs, the Swiss Agency for
Development and Cooperation, UK International Development, and the US Agency for
International Development. The findings, interpretations, and conclusions expressed here are
entirely those of the authors. They do not necessarily represent the views of the International
Bank for Reconstruction and Development/World Bank and its affiliated organizations or those
of the Executive Directors of the World Bank or the governments that they represent. Funding:
This work was supported by the Global Futures Laboratory, Arizona State University (J.S.F.)
Continuous infiltration and evolutionary trajectory of nuclear organelle DNA in<i>Oryza</i>
No full textTransfer of chloroplast or mitochondrial DNA into the nuclear genome is a common phenomenon in many species. However, little is known about the evolutionary fate and mechanism of transfer of organellar DNA sequences in higher plants. We observe abundant insertions of organelle DNA into the nuclear genomes of 22 genome assemblies across sevenOryzaspecies and further categorize nuclear organelle DNA (NORG) into 3406 orthologous groups. Analysis of the whole-genome resequencing data from 3458O. sativa,O. glaberrima, andO. barthiiaccessions indicate that NORGs have intra- and inter-population variability owing to sequence loss and transposon insertion during evolution. Our results also suggest that NORGs have been continuously produced during the evolution ofOryza, and both double-strand break repair pathways and replication-based mechanisms play important roles in integrating organelle DNA into the nuclear genome. Further investigation indicates that complex NORGs are formed through single mutational events before or during the insertion process via ligation of multiple plastid and/or mitochondrial DNA with each other. In summary, this work provides novel insights into the process of endosymbiotic DNA transfer and its role in reshaping genome variation and plant genome evolution
Twinner: Shining Light on Digital Twins in a Few Snaps
No full textWe present Twinner, the first large reconstruction model capable of recovering a scene’s illumination as well as an object’s geometry and material properties from only a few posed images. Twinner is based on the Large Reconstruction Model and innovates in three key ways: 1)We introduce a memory-efficient voxel-grid transformer whose memory scales only quadratically with the size of the voxel grid. 2) To address the scarcity of high-quality ground-truth PBR- shaded models, we introduce a large, fully synthetic dataset of procedurally generated PBR-textured objects lit with varied illumination. 3) To bridge the synthetic-to-real gap, we finetune the model on real-world datasets using a differentiable physically based shading model, eliminating the need for ground-truth illumination or material properties, which are challenging to obtain in real-world scenarios. We demonstrate the efficacy of our model on the real-world StanfordORB benchmark, where, given a few input views, we achieve reconstruction quality significantly superior to existing feed-forward reconstruction networks and comparable to slower per-scene optimization methods
Droplet Friction on Conformally Lubricated Surfaces
No full textWater-repellent technology has evolved to support various sectors of human activity. The key of water-repellency is reducing droplet friction, primarily generated from the contact between the solid surface and a moving droplet. Some types of water-repellent surfaces include superhydrophobic surfaces (SHS), which rely on the Cassie state to minimize contact via surface roughness; and liquid-infused surfaces (LIS), which use a lubricant layer to separate the droplet from the solid substrate. Although both surfaces effectively repel water, SHS can fail when the droplet penetrates the surface texture and transitions to Wenzel state, while LIS are prone to lubricant depletion. An intermediate condition, known as the slippery Wenzel state, remains underexplored. This state can occur on a conformally lubricated surface: a multiscale rough surface where the lubricant only infuses the nanoroughness instead of filling the microroughness.
Previous studies on conformally lubricated surfaces focused only on droplet mobility with various surface tension and different surface roughness. However, the behavior across a range of capillary number remains unknown. This study aims to unveil the droplet mobility on such surfaces by measuring the friction force using a cantilever probe method. Droplet velocity and oil viscosity were varied, yielding five different orders of magnitude in capillary number. Surfaces were fabricated by replicating the micropillared silicon wafer structure onto a transparent photopolymer, followed by coating with hydrophobic nanoparticles and spin-coating silicone oil at 10,000 rpm to achieve conformal lubrication.
This study compares the friction-capillary number relationship of SHS, LIS, and conformally lubricated surfaces made from the same base material. SHS and LIS results follow previously reported trends, but indicate that the lubricant layer is unstable. On conformally lubricated surfaces, droplets were found in both the slippery Wenzel and metastable slippery Cassie states. The slippery Wenzel state shows three distinct friction regimes (constant, linear, and nonlinear), with generally higher friction than LIS. Meanwhile, friction in the slippery Cassie state was lower than in LIS and SHS at the same capillary number, suggesting its potential for advanced water-repellent applications. Future work may focus on the origin of the three regimes, improving lubricant stability, and further exploring the slippery Cassie state
Developing novel fabrication techniques and device modeling for high-efficient III-nitride micro-sized light emitting diodes
No full textIII-nitride micro-sized light-emitting diodes (micro-LEDs) have advanced rapidly in recent years, with visible micro-LEDs based on InGaN materials and ultraviolet (UV) micro-LEDs based on AlGaN materials. In particular, InGaN micro-LEDs are considered strong candidates for next-generation display technology due to their high contrast, intense brightness, wide color gamut, and long lifespan. The reduction in device size has enabled higher resolution in display applications. UV micro-LEDs are considered to have tremendous application potential in various fields, including sterilization, bio-sensing, wireless communication, color conversion, and maskless lithography. Furthermore, compared to larger-sized LEDs, smaller-sized LEDs exhibit greater performance potential, which can significantly improve light extraction efficiency, current spreading, heat dissipation, communication modulation bandwidth, and data transmission rates.
This thesis seeks to address current challenges in InGaN and AlGaN micro-LEDs, including efficiency degradation due to sidewall damage and the high complexity of the micro-LED fabrication process. Our fabrication process has achieved micro-LED sizes as small as 2.3 microns. Research approaches span device modeling and fabrication, such as employing atomic layer etching techniques to eliminate the sidewall damage, achieving etching-free and low-complexity micro-LED fabrication through a novel selective thermal oxidation method, and optimizing carrier injection capability by polarization and band engineering. Research results indicate that these innovative technologies lead to significant performance enhancements for III-nitride micro-LEDs such as lower leakage current and higher efficiency. We believe these studies provide fresh insights into advanced techniques to fabricate high-performance micro-LEDs, paving the way for their future development and commercialization
A Hybrid Quantum–Classical Spectral Solver for Nonlinear Differential Equations
No full textWe investigate hybrid quantum–classical solvers for nonlinear boundary value problems using Chebyshev spectral collocation. Unlike prior methods such as H–DES, which repeatedly recompile circuits and encode the entire spectral basis on the quantum processor, our framework offloads only the residual minimisation to a quantum backend while retaining classical enforcement of boundary conditions. Two paradigms are considered: (i) gate-based residual minimisation on CUDA-Q using variational circuits to evaluate a Cubic Unconstrained Binary Optimisation (CUBO) cost, which naturally arises from the discretisation, and (ii) a Quadratic Unconstrained Binary Optimisation (QUBO) reformulation, which is required for execution on a quantum annealer, executed via a classical–quantum mapping. We further explore a CUBO extension on CUDA-Q and direct residual-to-energy mapping on annealers. Benchmarks confirm that the classical solver reproduces the analytic solution with spectral accuracy; among quantum-enhanced methods, the annealer-based QUBO yields the closest approximation. The gate-based CUBO solver improves upon a legacy variational baseline but exhibits a small interior bias due to limited circuit depth and precision. These findings underscore the complementary roles of annealers and gate-based devices in hybrid scientific computing and demonstrate a feasible workflow for the NISQ era rather than a speedup over classical methods. Recent progress in quantum algorithms for differential equations signals a rapidly maturing field with significant potential for practical quantum advantage.The author wish to thank Aditya Yadav, CEO of Automatski, for providing special access to the Automatski annealer simulators used in this work. His support and assistance were invaluable. The author would also like to thank Monica Van Dieren of NVIDIA for her guidance on using CUDA-Q
Connectivity of LEO Satellite Mega Constellations: An Application of Percolation Theory on a Sphere
No full textWith the advent of the 6 G era, global connectivity has become a common goal in the evolution of communications, aiming to bring Internet services to more unconnected regions. Additionally, the rise of applications such as the Internet of Everything and remote education also requires global connectivity. Non-terrestrial networks (NTN), particularly low earth orbit (LEO) satellites, play a crucial role in this future vision. Although some literature already analyze the coverage performance using stochastic geometry, the ability of generating large-scale continuous service area is still expected to analyze. Therefore, in this paper, we mainly investigate the necessary conditions of LEO satellite deployment for large-scale continuous service coverage on the earth. Firstly, we apply percolation theory to a closed spherical surface and define the percolation on a sphere for the first time. We introduce the sub-critical and super-critical cases to prove the existence of the phase transition of percolation probability. Then, through stereographic projection, we introduce the tight bounds and closed-form expression of the critical number of LEO satellites on the same constellation. In addition, we also investigate how the altitude and maximum slant range of LEO satellites affect percolation probability, and derive the critical values of them. Based on our findings, we provide useful recommendations for companies planning to deploy LEO satellite networks to enhance connectivity
An Adaptive Random Fourier Features approach Applied to Learning Stochastic Differential Equations
No full textThis work proposes a training algorithm based on adaptive random Fourier features (ARFF) with Metropolis sampling and resampling \cite{kammonen2024adaptiverandomfourierfeatures} for learning drift and diffusion components of stochastic differential equations from snapshot data. Specifically, this study considers It\^{o} diffusion processes and a likelihood-based loss function derived from the Euler-Maruyama integration introduced in \cite{Dietrich2023} and \cite{dridi2021learningstochasticdynamicalsystems}. This work evaluates the proposed method against benchmark problems presented in \cite{Dietrich2023}, including polynomial examples, underdamped Langevin dynamics, a stochastic susceptible-infected-recovered model, and a stochastic wave equation. Across all cases, the ARFF-based approach matches or surpasses the performance of conventional Adam-based optimization in both loss minimization and convergence speed. These results highlight the potential of ARFF as a compelling alternative for data-driven modeling of stochastic dynamics
Pharmacological targets and therapeutic mechanisms of Arabic gum in treating diabetic wounds: insights from network pharmacology and experimental validation.
No full textOn account of the long-term inflammatory microenvironment, diabetic wounds are challenging to heal in which advanced glycation end products are considered important factors hindering the healing of diabetic wounds. Gum Arabic has demonstrated significant potential in the treatment of various diseases owing to its anti-inflammatory and antioxidant properties. Nonetheless, there is still insufficient research on the role of Arabic gum in facilitating diabetic wounds healing and its mechanisms. This study aims to investigate the pharmacological targets and therapeutic mechanisms of Arabic Gum on diabetic wound healing by adopting network pharmacology, molecular docking, and experimental validation. Key active components of Arabic Gum and disease targets were identified through network pharmacology and bioinformatics. GO/KEGG enrichment was performed to identify critical pathways. Cytoscape and AutoDock were used for targets prediction and molecular docking validation. In vitro, Transwell assay and tube formation assay were performed to evaluate the effect of Arabic Gum on human fibroblasts migration and human umbilical vein endothelial cells angiogenesis. Western blotting analyzed Pro-caspase-1, ASC, NLRP3 and NF-κB pathway-related proteins. In vivo, a full-thickness diabetic wound model was established. Histological changes were assessed via H&E and Masson's staining, oxidative stress levels through DHE staining, inflammation levels with IL-1β, CD68 and CD206 staining, angiogenesis and cell proliferation levels were assessed by CD31 and Ki67 staining. The levels of pathway-related proteins were analyzed by NLRP3 and Phospho-NF-κB P65 staining. Network pharmacology analysis identified key targets, encompassing HSP90AA1, STAT3, and PRKCB, involved in the AGEs-NF-κB-NLRP3 signaling axis. Molecular docking demonstrated strong binding affinity between AG components and these targets. In vitro, AG lessened AGEs-induced activation of the NLRP3 inflammasome via modulation of the NF-κB pathway and reinforced cell migration and angiogenesis. In vivo, AG-treated diabetic wounds exhibited accelerated healing, with augmented collagen deposition, lowered oxidative stress and inflammation, and strengthened cell migration and angiogenesis. AG promotes diabetic wound healing by modulating the AGEs-NF-κB-NLRP3 axis, exerting anti-inflammatory, antioxidant, pro-angiogenic, and cell-proliferative effects. This study provides new insights into diabetic wound repair and suggests that AG is a promising therapeutic agent for improving diabetic wound healing.The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Support for
this research was provided by the Hubei Provincial Natural Science Foundation (Grants No. 2023AFB678). Zhongnan Hospital Fund
for Translational Medicine and Interdisciplinary Research (No. ZNJC202328). Science Foundation of Zhongnan Hospital,
Wuhan University (No. CXPY2020039