bluuebunny/crossref_metadata_2025_split huggingface dataset:
Separation of Water Level Change From Atmospheric Artifacts Through Application of Independent Component Analysis to InSAR Time Series
Authors: Belhadj‐aissa Saoussen , Simard Marc , Jones Cathleen E. , Oliver‐Cabrera Talib , Christensen Alexandra 
Published: June 2024

AbstractIn recent years, synthetic aperture radar (SAR) interferometry (InSAR) has emerged as a valuable tool for measuring water level change (WLC) to study hydrodynamic processes in coastal wetlands. However, the highly dynamic wet atmosphere conditions common in these areas have a significant impact on InSAR observations, producing errors in the derived values. Standard methods for estimating atmospheric noise in InSAR time series lack the spatial or temporal resolution needed to adequately correct for wet tropospheric delays. In this study, we utilize the Independent Component Analysis (ICA) signal decomposition technique to identify the likely WLC signal and eliminate atmospheric noise in a time series derived from rapid repeat measurements made with the L‐band uninhabited aerial vehicle synthetic aperture radar airborne instrument. The method compares in‐situ water level measurements with the independent components (IC) to identify the ICA components corresponding to WLC. The signal‐to‐noise ratio between the WLC after the ICA‐based filtering and in situ water level gauges used for validation reaches 16 dB compared to an average of 2.6 dB before filtering. The excluded IC are used to generate maps showing a time series of likely atmospheric features. The identified features in the maps generally correspond to atmospheric features identifiable in Next Generation Weather Radar (NEXRAD) S‐band ground weather radar reflectivity maps collected during the SAR acquisitions. The method is sufficiently general to be applied to any InSAR‐derived surface displacement time series.
Unraveling the Dynamics of Moisture Transport During Atmospheric Rivers Producing Rainfall in the Southern Andes
Authors: Mudiar Dipjyoti , Rondanelli Roberto , Valenzuela Raul A. , Garreaud René D. 
Published: June 2024

AbstractAtmospheric rivers (ARs) are known to produce both beneficial and extreme rainfall, leading to natural hazards in Chile. Motivated to understand moisture transport during AR events, this study performs a moisture budget analysis along 50 zonally elongated ARs reaching the western coast of South America. We identify the convergence of moist air masses of tropical/subtropical origin along the AR as the primary source of vertically integrated water vapor (IWV). Over the open ocean, moisture convergence is nearly balanced by precipitation. The advection of moisture along the AR, although smaller compared to mass convergence, significantly increases toward the landfalling region. The near conservation of IWV over the open ocean, observed by tracking a Lagrangian atmospheric column along the ARs, is the explanation behind the seemingly tropical origin of ARs in time‐lapse visualizations of IWV.
Reconstructed Late Summer Maximum Temperatures for the Southeastern United States From Tree‐Ring Blue Intensity
Authors: King Karen E. , Harley Grant L. , Maxwell Justin T. , Rayback Shelly , Cook Edward , Maxwell R. Stockton , Rochner Maegen L. , Bergan Ellen V. , Foley Zachary , Therrell Matthew , Bregy Joshua 
Published: June 2024

AbstractOver recent decades, the southeastern United States (Southeast) has become increasingly well represented by the terrestrial climate proxy record. However, while the paleo proxy records capture the region's hydroclimatic history over the last several centuries, the understanding of near surface air temperature variability is confined to the comparatively shorter observational period (1895‐present). Here, we detail the application of blue intensity (BI) methods on a network of tree‐ring collections and examine their utility for producing robust paleotemperature estimates. Results indicate that maximum latewood BI (LWBI) chronologies exhibit positive and temporally stable correlations (r = 0.28–0.54, p < 0.01) with summer maximum temperatures. As such, we use a network of LWBI chronologies to reconstruct August‐September average maximum temperatures for the Southeast spanning the period 1760–2010 CE. Our work demonstrates the utility of applying novel dendrochronological techniques to improve the understanding of the multi‐centennial temperature history of the Southeast.
Inclusion of Nonresonant Effects Into Quasi‐Linear Diffusion Rates for Electron Scattering by Electromagnetic Ion Cyclotron Waves
Authors: Shi Xiaofei , An Xin , Artemyev Anton , Zhang Xiao‐Jia , Mourenas Didier , Angelopoulos Vassilis 
Published: June 2024

AbstractElectromagnetic ion cyclotron (EMIC) waves are a key plasma mode affecting radiation belt dynamics. These waves are important for relativistic electron losses through scattering and precipitation into Earth's ionosphere. Although theoretical models of such resonant scattering predict a low‐energy cut‐off of ∼1 MeV for precipitating electrons, observations from low‐altitude spacecraft often show simultaneous relativistic and sub‐relativistic electron precipitation associated with EMIC waves. Recently, nonresonant electron scattering by EMIC waves has been proposed as a possible solution to the above discrepancy. We employ this model and a large database of EMIC waves to develop a universal treatment of electron interactions with EMIC waves, including nonresonant effects. We use the Green's function approach to generalize EMIC diffusion rates foregoing the need to modify existing codes or recompute empirical wave databases. Comparison with observations from the electron losses and fields investigation mission demonstrates the efficacy of the proposed method for explaining sub‐relativistic electron losses by EMIC waves.
ResACEUnet: An Improved Transformer Unet Model for 3D Seismic Fault Detection
Authors: Zu Shaohuan , Zhao Penghui , Ke Chaofan , Junxing Cao 
Published: June 2024

AbstractDetecting fault constitutes a pivotal aspect of seismic interpretation, significantly influencing the outcomes of petroleum and gas exploration. As artificial intelligence advances, convolutional neural network (CNN) has proven effective in detecting faults in seismic interpretation. Nevertheless, the receptive field of a convolutional layer within CNN is inherently limited, focusing on extracting local features, which lead to the detection of fewer and discontinuous fault features. In this study, integrating the local feature extraction capabilities of CNN with the global feature extraction prowess of transformer, we proposed a U‐shaped hybrid architecture model named ResACEUnet (Attention‐Convolution Unet with Efficient block) to detect fault of three‐dimensional (3D) seismic data. In ResACEUnet, we introduced a module called ACE block, which integrates convolution and attention mechanisms. This module enabled the model to simultaneously extract local features and model global contextual information, capturing more accurate fault features. In addition, we utilized a joint loss function named BCEDice loss, which composed of BCE (binary cross‐entropy) loss and dice loss to tackle the challenge of imbalanced positive and negative samples. The model was trained on a synthetic data set, with a range of data augmentation techniques were employed to bolster its generalization capabilities and robustness. We implemented our proposed method on the offshore F3 seismic data from the Netherlands and seismic data from Kerry3D and Parihaka in New Zealand. Compared to conventional popular models such as Unet, ResUnet, and SwinUnetR, ResACEUnet demonstrated superior capabilities in capturing more features and identifying fault with higher accuracy and continuity.
Heterodimensional Kondo superlattices with strong anisotropy
Authors: Feng Qi , Duan Junxi , Wang Ping , Jiang Wei , Peng Huimin , Zhong Jinrui , Cao Jin , Hu Yuqing , Li Qiuli , Wang Qinsheng , Zhou Jiadong , Yao Yugui 
Published: June 2024

AbstractLocalized magnetic moments in non-magnetic materials, by interacting with the itinerary electrons, can profoundly change the metallic properties, developing various correlated phenomena such as the Kondo effect, heavy fermion, and unconventional superconductivity. In most Kondo systems, the localized moments are introduced through magnetic impurities. However, the intrinsic magnetic properties of materials can also be modulated by the dimensionality. Here, we report the observation of Kondo effect in a heterodimensional superlattice VS2-VS, in which arrays of the one-dimensional (1D) VS chains are encapsulated by two-dimensional VS2 layers. In such a heterodimensional Kondo superlattice, we observe the typical Kondo effect but with intriguing anisotropic field dependence. This unique anisotropy is determined to originate from the magnetic anisotropy which has the root in the unique 1D chains in the structure, as corroborated by the first-principles calculation. Our results open up a novel avenue of studying exotic correlated physics in heterodimensional materials.
A broadly generalizable stabilization strategy for sarbecovirus fusion machinery vaccines
Authors: Lee Jimin , Stewart Cameron , Schäfer Alexandra , Leaf Elizabeth M. , Park Young-Jun , Asarnow Daniel , Powers John M. , Treichel Catherine , Sprouse Kaitlin R. , Corti Davide , Baric Ralph , King Neil P. , Veesler David 
Published: June 2024

AbstractEvolution of SARS-CoV-2 alters the antigenicity of the immunodominant spike (S) receptor-binding domain and N-terminal domain, undermining the efficacy of vaccines and antibody therapies. To overcome this challenge, we set out to develop a vaccine focusing antibody responses on the highly conserved but metastable S2 subunit, which folds as a spring-loaded fusion machinery. We describe a strategy for prefusion-stabilization and high yield recombinant production of SARS-CoV-2 S2 trimers with native structure and antigenicity. We demonstrate that our design strategy is broadly generalizable to sarbecoviruses, as exemplified with the SARS-CoV-1 (clade 1a) and PRD-0038 (clade 3) S2 subunits. Immunization of mice with a prefusion-stabilized SARS-CoV-2 S2 trimer elicits broadly reactive sarbecovirus antibodies and neutralizing antibody titers of comparable magnitude against Wuhan-Hu-1 and the immune evasive XBB.1.5 variant. Vaccinated mice were protected from weight loss and disease upon challenge with XBB.1.5, providing proof-of-principle for fusion machinery sarbecovirus vaccines.
Pulsed laser induced plasma and thermal effects on molybdenum carbide for dry reforming of methane
Authors: Li Yue , Liu Xingwu , Wu Tong , Zhang Xiangzhou , Han Hecheng , Liu Xiaoyu , Chen Yuke , Tang Zhenfei , Liu Zhen , Zhang Yuhai , Liu Hong , Zhao Lili , Ma Ding , Zhou Weijia 
Published: June 2024

AbstractDry reforming of methane (DRM) is a highly endothermic process, with its development hindered by the harsh thermocatalytic conditions required. We propose an innovative DRM approach utilizing a 16 W pulsed laser in combination with a cost-effective Mo2C catalyst, enabling DRM under milder conditions. The pulsed laser serves a dual function by inducing localized high temperatures and generating *CH plasma on the Mo2C surface. This activates CH4 and CO2, significantly accelerating the DRM reaction. Notably, the laser directly generates *CH plasma from CH4 through thermionic emission and cascade ionization, bypassing the traditional step-by-step dehydrogenation process and eliminating the rate-limiting step of methane cracking. This method maintains a carbon-oxygen balanced environment, thus preventing the deactivation of the Mo2C catalyst due to CO2 oxidation. The laser-catalytic DRM achieves high yields of H2 (14300.8 mmol h−1 g−1) and CO (14949.9 mmol h−1 g−1) with satisfactory energy efficiency (0.98 mmol kJ−1), providing a promising alternative for high-energy-consuming catalytic systems.
Inter-cell type interactions that control JNK signaling in the Drosophila intestine
Authors: Zhang Peng , Pronovost Stephen M. , Marchetti Marco , Zhang Chenge , Kang Xiaoyu , Kandelouei Tahmineh , Li Christopher , Edgar Bruce A. 
Published: June 2024

AbstractJNK signaling is a critical regulator of inflammation and regeneration, but how it is controlled in specific tissue contexts remains unclear. Here we show that, in the Drosophila intestine, the TNF-type ligand, Eiger (Egr), is expressed exclusively by intestinal stem cells (ISCs) and enteroblasts (EBs), where it is induced by stress and during aging. Egr preferentially activates JNK signaling in a paracrine fashion in differentiated enterocytes (ECs) via its receptor, Grindelwald (Grnd). N-glycosylation genes (Alg3, Alg9) restrain this activation, and stress-induced downregulation of Alg3 and Alg9 correlates with JNK activation, suggesting a regulatory switch. JNK activity in ECs induces expression of the intermembrane protease Rhomboid (Rho), driving secretion of EGFR ligands Keren (Krn) and Spitz (Spi), which in turn activate EGFR signaling in progenitor cells (ISCs and EBs) to stimulate their growth and division, as well as to produce more Egr. This study uncovers an N-glycosylation-controlled, paracrine JNK-EGFR-JNK feedforward loop that sustains ISC proliferation during stress-induced gut regeneration.
Detection of hidden antibiotic resistance through real-time genomics
Authors: Sauerborn Ela , Corredor Nancy Carolina , Reska Tim , Perlas Albert , Vargas da Fonseca Atum Samir , Goldman Nick , Wantia Nina , Prazeres da Costa Clarissa , Foster-Nyarko Ebenezer , Urban Lara 
Published: June 2024

AbstractReal-time genomics through nanopore sequencing holds the promise of fast antibiotic resistance prediction directly in the clinical setting. However, concerns about the accuracy of genomics-based resistance predictions persist, particularly when compared to traditional, clinically established diagnostic methods. Here, we leverage the case of a multi-drug resistant Klebsiella pneumoniae infection to demonstrate how real-time genomics can enhance the accuracy of antibiotic resistance profiling in complex infection scenarios. Our results show that unlike established diagnostics, nanopore sequencing data analysis can accurately detect low-abundance plasmid-mediated resistance, which often remains undetected by conventional methods. This capability has direct implications for clinical practice, where such “hidden” resistance profiles can critically influence treatment decisions. Consequently, the rapid, in situ application of real-time genomics holds significant promise for improving clinical decision-making and patient outcomes.
Uncovering the antiinflammatory potential of Lactiplantibacillus Plantarum fermented Cannabis Sativa L seeds
Authors: Shan LingYue , Tyagi Akanksha , Ham Hun-Ju , Oh Deog Hwan 
Published: June 2024

AbstractInflammation acts as a dual role in disease initiation and progression, while Cannabis sativa L. (hemp) seeds, known for their abundance of anti-inflammatory phytochemicals, present a promising food source. Additionally, fermentation may optimize the food matrix, thereby augmenting its developmental prospects. This study explores the anti-inflammatory potential of hemp seeds fermented with 10 different probiotic strains. Among these, Lactiplantibacillus plantarum fermented hemp seeds (FHS) demonstrated a significant anti-inflammatory ability, accompanied by a reduction in the expression of critical inflammatory markers such as TLR4, NF-κBp65, and iNOS. Moreover, there is a noteworthy dose-dependent inhibition of inflammatory cytokines TNF-α, IL-6, IL-1β, and NO within a concentration range of 50 to 500 µg/mL. Subsequently, metabolomics analysis using UHPLC-QTOF-MS highlighted significant metabolic alterations in FHS compared to raw hemp seeds (RHS). Through multivariate, univariate, and correlation analyses, indolelactic acid (IA) and homovanillic acid (HVA) emerged as the main anti-inflammatory metabolites in FHS. Validation via HPLC confirmed the concentration of IA and HVA in RHS and FHS and both organic acids demonstrated lower IC50 values for TNF-α, IL-1β, IL-6, IL-18, and NO inhibition, showcasing their potent anti-inflammatory abilities. Furthermore, in vitro gastro-intestinal digestion coupled with the Caco-2 cell monolayer model validates the uptake and bioaccessibility of FHS, further affirming IA and HVA as major anti-inflammatory compounds. Overall, this research sets the stage for the development of novel hemp seed-based products targeting inflammation-associated disorders.
Molecular-level hybridization of single-walled carbon nanotubes and a copper complex with counterbalanced electrostatic interactions
Authors: Kim Mi-Jeong , Kim Hyeonbeom , Kim Jungmo , Lee Yeo Jin , Lee Wonki , Hwang Jun Yeon , Kim Ki Kang , Jeong Hee Jin 
Published: June 2024

AbstractHybridization and wet processibility are highly desired development strategies for next-generation nanomaterials. In particular, the hybridization of carbon nanotubes (CNTs) and transition metals has been investigated for decades owing to the numerous advantages, such as high mechanical and electrical properties. However, manufacturing nano-hybridized CNTs/transition metals is complicated, and no studies have been reported on the dispersion and hybridization of transition metals with single-walled CNTs (SWCNTs) without any harsh or destructive methods due to the strong van der Waals forces. Herein, we demonstrate a one-step dispersion/hybridization of SWCNTs and a Cu-based complex and provide a mechanism derives from counterbalancing the electrostatic interactions via molecular-level charge transfer. The Cu-based complex-hybridized SWCNTs self-assemble and demonstrate suitable viscoelastic behaviors for various printing or coating processes. Finally, the nanostructured SWCNTs/Cu nanoparticle exhibits multifunctional electrothermal properties, electromagnetic interference shielding performances, and flexibilities. The proposed metal-complex-hybridized SWCNTs dispersions provide a wet process guideline for producing nanostructured electrodes.
Shift of the reduction potential of nickel(ii) Schiff base complexes in the presence of redox innocent metal ions
Authors: Bhunia Pradip , Gomila Rosa M. , Frontera Antonio , Ghosh Ashutosh 
Published: June 2024

Apart from the Lewis acidity of redox-inactive metal ions, the local electric field, especially for bivalent metal ions, and the size of the chelate ring formed by the ligand can also impact the reduction potential of NI(ii/i) pair.
Configuration and operation model for integrated energy power station considering energy storage
Authors: Li Qingxin , Shi Ming , Xie Yaoze , Zheng Linfeng 
Published: June 2024

AbstractLarge‐scale integration of renewable energy in China has had a major impact on the balance of supply and demand in the power system. It is crucial to integrate energy storage devices within wind power and photovoltaic (PV) stations to effectively manage the impact of large‐scale renewable energy generation on power balance and grid reliability. However, existing studies have not modelled the complex coupling between different types of power sources within a station. This article first analyses the costs and benefits of integrated wind–PV‐storage power stations. Considering the lifespan loss of energy storage, a two‐stage model for the configuration and operation of an integrated power station system is established to maximize the daily average net profit of the station. Furthermore, simulation is done to obtain the optimal configuration for integrated wind–PV‐storage power stations. The results indicate that considering the lifespan loss of storage can enhance the integration of renewable energy. It also improves the charging and discharging strategies of storage devices, extending their actual lifespan from 4.93 to 7.79 years and increasing the investment return rate of the station by 2.4%.
Adaptive identification of critical nodes for fault‐on voltage support in islanded microgrids
Authors: Cao Shiran , Zhu Lipeng , Li Jiayong , Huang Wen , He Lili , Zhang Wei , Zhao Huimin , Shuai Zhikang 
Published: June 2024

AbstractThe shedding of critical distributed energy resources during faults in an islanded microgrid may induce widespread voltage drops, potentially triggering a cascade of reactions leading to the collapse of the entire system. Accurately identifying critical nodes is the key technology to improve the resilience of microgrids. However, multi‐source coupling and the uncertainty in fault‐induced voltage sag can diminish the accuracy of node importance identification. To address this, this paper proposes an adaptive node identification method designed for quick and accurate identification of nodes that cope with various fault scenarios. This method introduces an index for evaluating voltage support capability based on the equivalent voltage drop range. This index adapts to fault uncertainty while integrating electrical parameters with spatial position. Furthermore, a higher‐order transition matrix reconstruction strategy with power propagation characteristics is proposed to reduce the higher‐order complexities arising from remote end faults' current flowing path length. Ultimately, the transition matrix is optimized by integrating it with the PageRank algorithm and highlighting the importance of source nodes. The proposed method is validated by numerical computation and time‐domain simulation results in a benchmark test microgrid, demonstrating its remarkable identification accuracy in a variety of fault scenarios.
Artificial Intelligence applications in Noise Radar Technology
Authors: Sénica Afonso L. , Marques Paulo A. C. , Figueiredo Mário A. T. 
Published: June 2024

AbstractRadar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use.