bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient hybrid numerical modeling of the seismic wavefield in the presence of solid-fluid boundaries
Authors: Lyu Chao , Romanowicz Barbara , Zhao Liang , Masson Yder 
Published: February 2025

Abstract
Applying full-waveform methods to image small-scale structures of geophysical interest buried within the Earth requires the computation of the seismic wavefield over large distances compared to the target wavelengths. This represents a considerable computational cost when using state-of-the-art numerical integration of the equations of motion in three-dimensional earth models. “Box Tomography” is a hybrid method that breaks up the wavefield computation into three parts, only one of which needs to be iterated for each model update, significantly saving computational time. To deploy this method in remote regions containing a fluid-solid boundary, one needs to construct artificial sources that confine the seismic wavefield within a small region that straddles this boundary. The difficulty arises from the need to combine the solid-fluid coupling with a hybrid numerical simulation in this region. Here, we report a reconciliation of different displacement potential expressions used for solving the acoustic wave equation and propose a unified framework for hybrid simulations. This represents a significant step towards applying ’Box Tomography’ in arbitrary regions inside the Earth, achieving a thousand-fold computational cost reduction compared to standard approaches without compromising accuracy. We also present examples of benchmarks of the hybrid simulations in the case of target regions at the ocean floor and the core-mantle boundary.
BMP signaling promotes zebrafish heart regeneration via alleviation of replication stress
Authors: Vasudevarao Mohankrishna Dalvoy , Posadas Pena Denise , Ihle Michaela , Bongiovanni Chiara , Maity Pallab , Geissler Dominik , Mohammadi Hossein Falah , Rall-Scharpf Melanie , Niemann Julian , Mommersteeg Mathilda T. M. , Redaelli Simone , Happ Kathrin , Wu Chi-Chung , Beisaw Arica , Scharffetter-Kochanek Karin , D’Uva Gabriele , Malek Mohammadi Mona , Wiesmüller Lisa , Geiger Hartmut , Weidinger Gilbert 
Published: February 2025

Abstract
In contrast to mammals, adult zebrafish achieve complete heart regeneration via proliferation of cardiomyocytes. Surprisingly, we found that regenerating cardiomyocytes experience DNA replication stress, which represents one reason for declining tissue regeneration during aging in mammals. Pharmacological inhibition of ATM and ATR kinases revealed that DNA damage response signaling is essential for zebrafish heart regeneration. Manipulation of Bone Morphogenetic Protein (BMP)-Smad signaling using transgenics and mutants showed that BMP signaling alleviates cardiomyocyte replication stress. BMP signaling also rescues neonatal mouse cardiomyocytes, human fibroblasts and human hematopoietic stem and progenitor cells (HSPCs) from replication stress. DNA fiber spreading assays indicate that BMP signaling facilitates re-start of replication forks after replication stress-induced stalling. Our results identify the ability to overcome replication stress as key factor for the elevated zebrafish heart regeneration capacity and reveal a conserved role for BMP signaling in promotion of stress-free DNA replication.
Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography
Authors: Venkatakrishnan Singanallur , Rahman Obaidullah , Amichi Lynda , Arregui-Mena Jose D. , Yu Haoran , Cullen David A. , Ziabari Amirkoushyar 
Published: February 2025

Abstract
Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse and low signal-to-noise ratio of the measurements. In addition, small metal particles can cause strong streaking and shading artifacts in the 3D reconstructions when using conventional reconstruction algorithms due to the presence of Bragg diffraction and the large scattering cross-section difference between the materials of the particles and the background support regions. These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. In this paper, we present a two-stage algorithm based on metal artifact reduction, utilizing model-based iterative reconstruction methods with adaptive adjustment of regularization parameters. Our approach yields high-quality 3D reconstructions compared to traditional algorithms, accurately capturing both the metal particles as well as the background support. We demonstrate the effectiveness of our algorithm through simulated and experimental bright-field electron tomography data, showing significant improvements in reconstruction quality compared to traditional methods.
The Rhizophagus irregularis permease RiFTR1 functions without a ferroxidase partner for reductive iron transport
Authors: Tamayo Elisabeth , López-Lorca Víctor Manuel , Shim Chaeeun , López-Castillo Olga , Castillo Araceli G. , Requena Natalia , Benz J. Philipp , Ferrol Nuria 
Published: February 2025

Abstract
The contribution of arbuscular mycorrhizal fungi (AM fungi) to plant iron (Fe) acquisition has been demonstrated in several studies. A previous investigation revealed that the AM fungus Rhizophagus irregularis utilizes a high-affinity reductive pathway for Fe uptake, mediated by the Fe transporter RiFTR1. In this study, we used a genome-wide approach in R. irregularis to find genes encoding ferroxidases of the multicopper oxidase (MCO) gene family in an attempt to identify the ferroxidase partner of RiFTR1. Nine genes putatively encoding MCOs (RiMCO1-9) were identified. Yeast complementation assays demonstrated that RiMCO1 and RiMCO3 can function as ferroxidases, suggesting their involvement in the reductive Fe uptake pathway. Surprisingly, RiFTR1 was capable of transporting Fe in yeast without a ferroxidase partner, resembling the Fe transport mechanism of plant IRT1-like systems. RiFTR1 exhibited increase expression in arbuscules. Overexpression of RiFTR1 in Medicago truncatula roots led to enhanced mycorrhizal colonization and arbuscule abundance, highlighting the significance of Fe for AM symbiosis.
Integrated anti-electronics for positron annihilation spectroscopy
Authors: Guatieri Francesco 
Published: February 2025

Abstract
Imaging the features of a sample using Positron Annihilation Spectroscopy (PAS) is currently achieved by rastering, i.e. by scanning the sample surface with a sharply focused positron beam. However, a beam of arbitrary shape (sculpted beam) would allow the application of more versatile single-pixel imaging (SPI) techniques. I introduce the design of a microelectronic device employing a 2D array of Zener diodes as an active positron moderator, capable of sculpting positron beams with
$${6}\,\upmu \hbox {m}$$
resolution. The re-emitted positrons are accelerated towards the sample through a miniaturized electrostatic lens system and reaching
$${100}\,\hbox {nm}$$
resolution. The fast switch-on (
$${90}\,\hbox {ps}$$
) and switch-off (
$${250}\,\hbox {ps}$$
) time of the device would enable state-of-the-art positron annihilation lifetime spectroscopy (PALS) and PAS imaging with high spatial and temporal resolution.
A deep learning framework based on structured space model for detecting small objects in complex underwater environments
Authors: Zhuang Yaoming , Liu Jiaming , Zhao Haoyang , Ma Longyu , Fang Zirui , Li Li , Wu Chengdong , Cui Wei , Liu Zhanlin 
Published: February 2025

Abstract
Regular monitoring of marine life is essential for preserving the stability of marine ecosystems. However, underwater target detection presents several challenges, particularly in balancing accuracy with model efficiency and real-time performance. To address these issues, we propose an innovative approach that combines the Structured Space Model (SSM) with feature enhancement, specifically designed for small target detection in underwater environments. We developed a high-accuracy, lightweight detection model—UWNet. The results demonstrate that UWNet excels in detection accuracy, particularly in identifying difficult-to-detect organisms like starfish and scallops. Compared to other models, UWNet reduces the number of model parameters by 5% to 390%, substantially improving computational efficiency while maintaining top detection accuracy. Its lightweight design enhances the model’s applicability for deployment on underwater robots.
DeepServo: Deep learning‐enhanced state feedback for robust servo system control
Authors: Amiri Farhad , Eskandari Mohsen , Moradi Mohammad H. 
Published: February 2025

AbstractServo controllers are essential components in robotics, manufacturing, and various industrial applications. However, achieving fast and accurate reference tracking in servo systems remains challenging due to modelling uncertainties and external disturbances. In this paper, a hybrid control strategy is proposed that combines a Linear Quadratic Regulator (LQR) state‐feedback controller with deep learning to address these challenges. The LQR controller utilises system state measurements to optimise the control input, while the integration of a deep neural network enhances accuracy and dynamic response by adapting to changing system conditions. This approach provides robust control performance, effectively mitigating the impact of uncertainties and disturbances on servo system behaviour. The proposed method was validated using AC servo motors, among the most common servo systems, though the approach is adaptable to other servo‐like systems. Comparative evaluations are conducted against existing methods, including SIMC‐SMC, 2DOF‐IMC‐SMC, 2DOF‐IMC‐PID, and SIMC‐PD controllers, focusing on the angular position control of a servo motor. Simulation results demonstrate that the proposed controller outperforms these methods in terms of robustness, precision, and disturbance rejection. These findings highlight the potential of the proposed LQR‐deep learning framework to significantly improve servo system performance across a wide range of applications.
Combined use of long short‐term memory neural network and quantum computation for hierarchical forecasting of locational marginal prices
Authors: Huang Xin , Liu Guozhong , Huan Jiajia , Luo Shuxin , Qiu Jing , Qin Feiyan , Xu Yunxia 
Published: February 2025

AbstractAccurate locational marginal price forecasting (LMPF) is crucial for the efficient allocation of resources. Nevertheless, the sudden changes in LMP make it inadequate for many existing long short‐term memory (LSTM) network‐based prediction models to achieve the required accuracy for practical applications. This study adopts a hierarchical method of three layers based on double quantum‐inspired grey wolf optimisation (QGWO) to improve the LSTM model (HD‐QGWO‐LSTM) for a one‐step LMPF. The top layer completes the data processing. The middle layer is a QGWO‐optimised support vector machine (SVM) for classifing whether LMPs are price spikes. The bottom laver is a double QGWO‐improved LSTM (QGWO‐LSTM) model for a real LMPF, where one QGWO‐LSTM is for the spike LMPF and the other is for the non‐spike LMPF. To address the issue of excessively long training times during the design of the LSTM network structure and parameter selection, a QGWO algorithm is proposed and used to optimise four LSTM parameters. The simulation results on the New England electricity market show that the HD‐QGWO‐LSTM method achieves similar prediction accuracy to other four LSTM‐based methods. The results also validate that the QGWO algorithm significantly reduces time consumption while ensuring optimisation effectiveness when optimising SVM and LSTM.
Long Short‐Term Memory Network for Co‐Frequency Co‐Time Full‐Duplex Digital Domain Interference Suppression
Authors: Zhang Yuting , Ma Chao , Gao Xiaoyan , Huang Yuhan 
Published: February 2025

ABSTRACTThe co‐frequency co‐time full duplex (CCFD) system transmits and receives signals at the same frequency simultaneously, which can effectively improve the communication spectrum utilization of satellite and solve the problem of satellite frequency resource constraint. However, due to a large number of devices in a relatively small space, there is strong self‐interference between transmitting and receiving antennas. Besides, the received signal intensity is not stable due to other factors such as multipath effect, electromagnetic interference, and so on. To solve this problem, this paper proposes a machine learning method for self‐interference suppression of CCFD systems in digital domain. A signal sequence decomposition method based on the number of poles is used to solve the problem of insufficient suppression of nonlinear interference signal caused by signal strength instability. By using Hilbert transform, the input signal dimension of machine learning is reduced, which is to solve the high computational complexity. The short‐term and long‐term neural network architecture is adopted to predict and reconstruct the interference, and Bayesian optimization method is used to optimize the hyperparameters of the network, and introduces the acquisition function into the loss function, to improve the comprehensive training optimization of machine learning networks. The experimental results show that the proposed algorithm can effectively analyze and suppress the interference signal, and the interference suppression capability is improved by 10.7 dB compared with the traditional Long Short‐Term Memory algorithm and 21.1 dB compared with the Least Mean Square algorithm. The algorithm presented in this paper is significant for the application of CCFD system on satellite.
Simulating pearl millet growth and yield with DSSAT-CERES-millet model: Evaluating climate-smart practices for uptake across diverse environments in Senegal
Authors: Zagre Inoussa , Worou Omonlola N. , Akinseye Folorunso M. , Joseph Jacob E. , Faye Aliou , Kone Mama , Whitbread Anthony M. 
Published: February 2025

Abstract

Background:
Evaluating crop suitability across diverse agro-climatic zones is crucial for mitigating risks to agricultural yield under climate change, particularly for pearl millet production in Senegal. To facilitate informed tactical or strategic decision making, aimed at enhancing productivity, a modeling approach is essential for comprehending climate impacts and formulating effective adaptation strategies.
Methods:
The DSSAT model’s capability to simulate four pearl millet cultivars (Souna-3, SL 28, SL 423, Thialack-2) under varying plant populations and micro-dosing fertilization strategies in diverse environments is evaluated in this study. The research also examines the risk associated with management practices on millet productivity in the context of climate variability and changing conditions for the period 1990–2020. To achieve these objectives, the CERES-millet model was calibrated using field data from 2022 to 2023 growing seasons for the four cultivars and subsequently validated with independent datasets from on-farm demonstrations in Senegal under various organo-mineral fertilizer applications for the same seasons.
Results:
The CERES-Millet model demonstrated high accuracy, simulating key growth stages and yield components across various millet cultivars and environments. For anthesis and physiological maturity, the model achieved low RMSE values (3–5 days and 2–4 days) and acceptable d-index ranges (from 0.6 to 0.9), indicating reliable predictions of these critical developmental milestones. The model’s performance extended to accurately simulating leaf area index (LAI), grain yield, and above-ground biomass, with specific RMSE values reported for different cultivars such as Souna 3 (RMSE of 443 kg ha
−1
for grain yield and 747 kg ha
−1
for AGB), Thialack-2 estimated RMSE of 389 kg ha
−1
for grain yield and 208 kg ha
−1
for AGB. SL 28 recorded RMSE of 257 kg ha
−1
for grain yield and 560 kg ha
−1
for AGB, while SL 423 had RMSE of 698 kg ha
−1
for grain and 640 kg ha
−1
for AGB. A long-term 31-year seasonal simulation revealed that dual-purpose varieties consistently outperformed Souna 3 in terms of yield, despite significant inter-annual variability.
Conclusion:
Combining these varieties with micro-dosing fertilizer applications led to higher yield thresholds. Notably, micro-dosing fertilizer level 3 (3g/hill of NPK, 2g/hill of Urea, and 50g/hill of cow manure) demonstrated consistent stability, making it a cost-effective approach to mitigate yield variability in semi-arid regions of Senegal. Long-term simulations highlighted the promising potential of combining dual-purpose varieties with optimized micro-dosing strategies to enhance yield stability and resilience. This approach offers a practical method for mitigating the effects of climate variability in Senegal and similar environmental contexts.
Physiological, biochemical and genetic studies reveal differing responses of West Africa rice genotypes under induced upland field and greenhouse drought stresses
Authors: Adjah Kossi Lorimpo , Asante Maxwell Darko , Toure Aboubacar , Aziadekey Mawuli , Frei Michael , Wu Linbo , Wairich Andriele , Yadav Shailesh , Gamenyah Daniel Dzorkpe , Vaziritabar Yavar 
Published: February 2025

Abstract

Background
: Drought is the single most important factor limiting rainfed upland rice production in Africa and is aggravated by climate change leading to a serious food security threat in the continent. This study aimed to assess the impact of drought stress on morpho-physiological and biochemical traits among rice genotypes and quantify the genetic variability among the genotypes under both reproductive drought stress (RS) and non-stress (NS) conditions. Further, this study aimed to select drought-tolerant genotypes and identify traits to monitor during screening for drought under field and greenhouse conditions.
Methods
: To achieve these objectives, we first screened 100 genotypes under upland reproductive stage drought field conditions as well as non-stress conditions using α-lattice design with three replications. Out of the 100, we selected 14 genotypes based on yield and yield-related traits under both conditions (RS and NS) for further screening under drought in the greenhouse condition using completely randomized block design (RCBD) with three replicates. Data on yield-related traits, relative water content (RWC), malondialdehyde (MDA), proline and genetic diversity parameters were collected.
Results
: Under the field screening, for all the investigated traits, a general reduction was observed among the genotypes under drought stress compared to non-stress conditions. Traits such as spikelet fertility (SFP), grain yield per plant (GYP), hundred grain weight (HGW), leaf rolling score, and leaf drying score recorded high broad-sense heritability (>60%) combined with high genetic advance (>20%) implying that selection could be rewarding for these traits under drought stress. Grain yield was positively associated with most yield-related traits under both water regimes. Under the greenhouse screening, RWC of all genotypes was reduced by >14%, except G11 (1.58%), G99 (8.43%), and G100 (5.43%), while increased MDA of >45% for all genotypes was observed, except G11 (25.15%), G99 (39.37%), and G100 (43.65%) under drought.
Conclusions
: Based on the correlation analysis, principal component analysis, and the multi-trait genotype-ideotype distance index (MGIDI) using relative trait values, grain yield, spikelet fertility, grain length, MDA and RWC are recommended in descending order for monitoring during screening for drought tolerance. Overall, G60, G100, G99, and G11 were selected in chronological order as genotypes with enhanced tolerance to drought. Among these genotypes, G60 (Viwornor short) is glaberrima ecotype, while G100 (UPLR-17), G99 (APO), and G11 (CRI-Enapa) are indica ecotypes.
Randomized block coordinate descent method for linear ill-posed problems
Authors: Jin Qinian , Liu Duo 
Published: February 2025

Abstract
Consider the linear ill-posed problems of the form

∑

i
=
1

b

A
i

x
i

=
y

, where, for each i, Ai
is a bounded linear operator between two Hilbert spaces Xi
and

Y

. When b is huge, solving the problem by an iterative method using the full gradient at each iteration step is both time-consuming and memory insufficient. Although randomized block coordinate descent (RBCD) method has been shown to be an efficient method for well-posed large-scale optimization problems with a small amount of memory, there still lacks a convergence analysis on the RBCD method for solving ill-posed problems. In this paper, we investigate the convergence property of the RBCD method with noisy data under either a priori or a posteriori stopping rules. We prove that the RBCD method combined with an a priori stopping rule yields a sequence that converges weakly to a solution of the problem almost surely. We also consider the early stopping of the RBCD method and demonstrate that the discrepancy principle can terminate the iteration after finite many steps almost surely. For a class of ill-posed problems with special tensor product form, we obtain strong convergence results on the RBCD method. Furthermore, we consider incorporating the convex regularization terms into the RBCD method to enhance the detection of solution features. To illustrate the theory and the performance of the method, numerical simulations from the imaging modalities in computed tomography and compressive temporal imaging are reported.
A high Q factor MEMS accelerometer with quasi-zero effective stiffness
Authors: Ma Zhipeng , Dong Leinuo , Ye Ziyi , Jin Yiming , Li Mingkang , Qian Xinyu , Zheng Xudong , Jin Zhonghe 
Published: February 2025

Abstract
This paper presents a high-performance MEMS accelerometer with ultralow noise and wide bandwidth based on a force control of a mechanical system with high quality-factor (Q factor) and quasi-zero effective stiffness. Both dynamic model and noise model of the proposed accelerometer were developed. Based on the dynamic model, a new damping controller and a phase compensation controller are proposed to improve the bandwidth for the high Q factor accelerometers. The noise model is verified experimentally, indicating that the readout circuit noise and mechanical thermal noise are greatly reduced by the decrease of the effective stiffness and air damping, respectively. The accelerometer with phase compensation controller exhibits a linear relationship between the noise power and the bandwidth, which can be tuned by adjusting the controller parameters. Finally, the bandwidth of ranging from 130 to 381 Hz and the base noise of ranging from 140 ng √Hz−1 to 860 ng √Hz−1 are achieved for the proposed accelerometer.