bluuebunny/crossref_metadata_2025_split huggingface dataset:
Corn leaf disease recognition based on improved EfficientNet
Authors: Sun Xiaowei , Huo Hua 
Published: January 2025

AbstractMaize leaf disease seriously affects maize yield, a maize leaf disease identification model with an improved lightweight network EfficientNet was proposed in this study. First, the model replaces the SENet module in the MBConv module with the CBAM module, so that the model not only focuses on the correlation between the channels but also adaptively learns the attentional weight of each spatial location. Furthermore, a multi‐scale feature fusion layer based on residual connection is introduced to extract more comprehensive and richer disease features at different scales. Finally, by introducing the double pooling method, the overall feature distribution is smoothed while highlighting the important disease features. After the three improvements, the model's recognition accuracy on the test set increased by 2.34%, 2.16%, and 0.97%, respectively, and the improved model achieved an average recognition accuracy of 98.32%, an average precision of 98.29%, and an average recall of 98.25%. The experimental results compared with other models show that the average recognition accuracy of the proposed model is 5.23%, 3.68%, 1.99%, 1.79%, and 3.2% higher than ResNet34, DenseNet121, MobileNet V2, SqueezeNet, and EfficientNet B0, respectively. Activation heat maps show that the improved model can effectively suppress background interference.
Highly accurate real-space electron densities with neural networks
Authors: Cheng Lixue , Szabó P. Bernát , Schätzle Zeno , Kooi Derk P. , Köhler Jonas , Giesbertz Klaas J. H. , Noé Frank , Hermann Jan , Gori-Giorgi Paola , Foster Adam 
Published: January 2025

Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation. We use variational quantum Monte Carlo with deep-learning Ansätze to obtain highly accurate wave functions free of basis set errors and from them, using our novel method, correspondingly accurate electron densities, which we demonstrate by calculating dipole moments, nuclear forces, contact densities, and other density-based properties.
Low-vibration cryogenic test facility for next generation of ground-based gravitational-wave observatories
Authors: Kapasi D. P. , McRae T. G. , Eichholz J. , Altin P. A. , McClelland D. E. , Slagmolen B. J. J. 
Published: January 2025

We present the design and commissioning of a cryogenic low-vibration test facility that measures displacement noise from a gram-scale silicon cantilever at the level of 10−16m/Hz at 1 kHz. This sensitivity is necessary for future tests of thermal noise models on cross sections of silicon suspension samples proposed for future gravitational-wave detectors. A volume of ∼36 l is enclosed by radiation shields cooling an optical test cavity that is suspended from a multi-stage pendulum chain providing isolation from acoustic and environmental noise. This 3 kg test cavity housing a crystalline silicon cantilever is radiatively cooled to 123 K in 41 h and held at that temperature over many months with a relative temperature stability of ±1 mK. The facility housing the test cavity is sensitive to cavity length changes, which can resolve thermal fluctuations at the desired sensitivity. It is capable of interferometrically measuring temperature-dependent broadband displacement noise directly between 50 Hz and 10 kHz, where current and future ground-based gravitational wave observatories are the most sensitive. With a suitable cantilever design, the cryogenic facility we describe here will allow for the measurement of broadband thermal noise in crystalline silicon at 123 K. This will guide the design of suspensions in planned future cryogenic ground-based gravitational-wave detectors such as LIGO Voyager and may have implications for suspensions in the Einstein Telescope. This facility is also suitable for the testing of new mirror coatings at cryogenic temperatures.
Achieving multi-unit turns
Authors: Afshari Saleh Reihaneh , Taleghani-Nikazm Carmen , Monfaredi Elham , Rezaee Parvaneh 
Published: January 2025

Abstract
This paper provides a conversation analytical and multimodal examination of a highly ubiquitous Persian token,
khob, in everyday Persian multi-unit tellings. Based upon corpora of daily interactions between family
members and friends over phone and face-to-face, three functions of khob are identified: (a)
khob can be used by the recipient to a telling as a response functioning as a continuer and acknowledgement
token, passing the opportunity for speakership, prompting the next unit of telling, and acknowledging the delivered prior turn. In
this function, khob can carry a rising or falling final pitch movement; (b) the token can also be used as a tag
by the speaker of a telling to elicit recipiency and check the recipient’s understanding of the turn so far. As a tag,
khob may or may not solicit a response. Response soliciting and non-soliciting khobs differ
in terms of the participants’ gaze behaviour and the coparticipants’ level of engagement in the storytelling, but both types only
appear with a final rise in our corpora. Finally, (c) khob can be used as a resumption marker, managing a return
to storytelling after it is suspended with an intervening action. Interactions were recorded in Iran.
Current-induced spin polarisation in Rashba–Dresselhaus systems under different point groups
Authors: Dey Akash , Nandy Ashis K , Saha Kush 
Published: January 2025

Abstract
Non-magnetic materials without inversion symmetry typically exhibit strong Rashba spin–orbit coupling (SOC), enabling the well-known Rashba Edelstein effect where an external electrical current induces transverse spin polarisation. In this study, we demonstrate that electrically induced spin polarisation in non-magnetic materials, for example, electronic systems within quantum-well geometries, can significantly be influenced by the system’s point-group symmetries, such as Cn
and C

nv
. These symmetries allow various linear and higher-order momentum,

k
−

varying SOC Hamiltonian. Specifically, we show that surfaces having C

n
point-group symmetry, which permits specific linear and cubic Rashba and Dresselhaus SOC terms, can lead to both orthogonal and non-orthogonal spin polarisations with respect to the applied field. In contrast, surfaces with C

nv
symmetry exhibit only transverse spin polarisation, regardless of the linear and cubic SOC terms. We further find contrasting spin polarisation for cubic-in-k SOC as compared to the linear-in-k SOC when energy is varied, for example, through doping. Additionally, we show that the surfaces with C

n
symmetry may exhibit persistent spin current, depending on the relative strength between different momentum-dependent SOC terms. Our finding emphasizes the significance of crystal symmetry in understanding and manipulating induced spin polarisation in noncentrosymmetric materials, especially in surface/interface systems.
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
Authors: de Santi Natalí S.M. , Villaescusa-Navarro Francisco , Raul Abramo L. , Shao Helen , Perez Lucia A. , Castro Tiago , Ni Yueying , Lovell Christopher C. , Hernández-Martínez Elena , Marinacci Federico , Spergel David N. , Dolag Klaus , Hernquist Lars , Vogelsberger Mark 
Published: January 2025

Abstract
It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood-free inference without imposing cuts on scale.
In particular, de Santi et al. [58] developed models that could accurately infer the value of Ωm from catalogs that only contain the positions and radial velocities of galaxies that are robust to different astrophysics and subgrid models.
However, observations are affected by many effects, including (1) masking, (2) uncertainties in peculiar velocities and radial distances, and (3) different galaxy population selections.
Moreover, observations only allow us to measure redshift, which entangles the galaxy radial positions and velocities. In this paper we train and test our models on galaxy catalogs, created from thousands of state-of-the-art hydrodynamic simulations run with different codes from the CAMELS project, that incorporate these observational effects.
We find that while such effects degrade the precision and accuracy of the models, the fraction of galaxy catalogs for which the models retain high performance and robustness is over 90%, demonstrating the potential for applying them to real data.
Stellar-mass black-hole binaries in LISA: characteristics and complementarity with current-generation interferometers
Authors: Buscicchio R. , Torrado J. , Caprini C. , Nardini G. , Karnesis N. , Pieroni M. , Sesana A. 
Published: January 2025

Abstract
Stellar-mass black-hole binaries are the most numerous gravitational-wave sources observed to date.
Their properties make them suitable for observation both by ground- and space-based detectors.
Starting from synthetic catalogues constructed based on
observational constraints from ground-based detectors, we
explore the detection rates and the characteristic parameters of the stellar-mass black-hole binaries observable by LISA during their inspiral, using signal-to-noise ratio thresholds as a detection criterion.
We find that only a handful of these sources will be detectable
with signal-to-noise ratio larger than 8:
about 5 sources on average in 4 years of mission duration, among which only one or two are multiband ones (i.e. merging in less than 15 years).
We find that detectable sources have chirp mass 10 M
⊙ ≲ ℳ
c
≲ 100 M
⊙, residual time-to-coalescence 4 yr ≲ τc
≲ 100 yr, and redshift z ≲ 0.1, much closer than those observed up to now by ground-based detectors.
We also explore correlations between the number of LISA detectable sources and the parameters of the population, suggesting that a joint measurement with the stochastic signal might be informative of the population characteristics.
By performing parameter estimation on a subset of sources from the catalogues, we conclude that, even if LISA measurements will not be directly informative on the population due to the low number of resolvable sources, it will characterise a few, low-redshift candidates with great precision.
Furthermore, we construct for the first time the LISA waterfall plot for low chirp-mass systems, as a function of their time to coalescence and inclination.
We demonstrate that LISA will also be able to
discriminate and characterize, through very precise parameter estimation, a population of binaries with higher masses, ℳ
c
∼ 𝒪(103) M
⊙, at the boundary of ground-based detectors sensitivity.
Bridging dark energy and black holes with EFT: frame transformation and gravitational wave speed
Authors: Mukohyama Shinji , Seraille Emeric , Takahashi Kazufumi , Yingcharoenrat Vicharit 
Published: January 2025

Abstract
Typically, constraints on parameters of the effective field theory (EFT) of dark energy have been obtained in the Jordan frame, where matter fields are minimally coupled to gravity. To connect these constraints with those of the EFT of black hole perturbations with a timelike scalar profile, it is necessary to perform a frame transformation on the EFT in general. In this paper, we study the conformal/disformal transformation of EFT parameters on an arbitrary background.
Furthermore, we explore the effect of an EFT operator M
6(r) σ̅

μ

ν

δK

ν

α

δK

α

μ
, which is elusive to the LIGO/Virgo bound on gravitational-wave speed, on the dynamics of odd-parity black hole perturbations. Intriguingly, a deviation from luminal propagation shows up only in the vicinity of the black hole, and the speeds of perturbations in the radial and angular directions are different in general due to the traceless part σ̅

μ

ν
of the background extrinsic curvature. This study establishes an important link between cosmological constraints and those obtained in the black hole regime.
Runaway electron beam formation, vertical motion, termination and wall loads in EU-DEMO
Authors: Vannini Francesco , Bandaru Vinodh Kumar , Bergström Hannes , Schwarz Nina , Artola F J , Hoelzl Matthias , Pautasso Gabriella , Nardon Eric , Maviglia Francesco , Richiusa M L , Emanuelli Enrico 
Published: January 2025

Abstract
Runaway electron loads onto material structures are a major concern for future large tokamaks due to the efficient avalanching at high plasma currents. &#xD;Here, we perform predictive numerical studies using the JOREK code for a plausible plasma configuration in the European DEMO fusion power plant, focusing in this paper on the scenario where the highest multi-ampere runaway electron beam is formed.&#xD;The work first comprises axisymmetric predictions of runaway electron beam formation in a mitigated scenario and of the simultaneous vertical motion of the beam due to loss of position control. The subsequent runaway electron beam termination triggered by a burst of MHD activity during the course of the vertical motion is then simulated in 3D with the runaway electron fluid self-consistently coupled to the MHD modes. Finally, the resulting deposition pattern of the runaway electrons onto wall structures is calculated with a relativistic test particle approach. This way, the suitability of a possible sacrificial limiter concept for the protection of first wall components is assessed.
Relaxation dynamics of integrable field theories after a global quantum quench
Authors: Salvo Emanuele Di , Schuricht Dirk 
Published: January 2025

Abstract
We apply the linked cluster expansion as well as the quench action approach to study the time evolution of one-point functions after a quantum quench in integrable field theories. We argue that the relaxation towards the stationary value fundamentally differs depending on the locality properties of the considered observable: while for local operators both exponential and power-law decaying terms are present in the dynamics, for semi-local operators the latter are absent. We explicitly confirm this for the Ising field theory, the sinh-Gordon model and the repulsive sine-Gordon model.
Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning
Authors: Eusebi Ryan , Su Hui , Wu Longtao , Rong Pingping , Balaguru Karthik , Leung Ruby , Choi Yong-Sang , Chan Pak Wai , Gan Jianping , DeMaria Mark , Chirokova Galina 
Published: January 2025

Abstract
Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is salinity, which is important for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the impact and efficacy of using salinity information in state-of-the-art forecasting, we use a statistical model consisting of a variety of machine learning (ML) methods. For salinity data, we use satellite measurements of pre-storm sea surface salinity (SSS) as a proxy for the salinity stratification. We train and test the model on various ocean basins, including the Atlantic, eastern North Pacific and western North Pacific. A calibrator is trained on top of the ML models to correct and enhance probability forecasts. The calibrator significantly improves probability forecasts relative to recent works. The ML model performance is improved with the addition of SSS in the Eastern North Pacific, western North Pacific, and the Caribbean subregion of the North Atlantic, and the overall model performance is better than previous studies. SSS decreases model skill for a model trained on the full Atlantic basin. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.
Mangrove biomass productivity and sediment carbon storage assessment at selected sites in Mauritius: the effect of tidal inundation, forest age and mineral availability
Authors: Doodee M D D , Rughooputh S D D V , Jawaheer S 
Published: January 2025

Abstract
Carbon dioxide is the most abundant anthropogenic greenhouse gas released in the environment and is considered as one of the main drivers of global warming and ensuing climate change. Mangals, recognised as key ‘blue carbon’ ecosystems, are amongst the world’s most carbon-dense ecosystems and can sequester a considerable amount of carbon through biological carbon sequestration in their biomass and soils. In this work, five Rhizophora mucronata natural and planted forests in Mauritius were studied with the aims at evaluating the carbon storage potential and the effect of tidal inundation, forest age, and mineral availability on biomass productivity and sediment carbon storage. Using 25m x 25m quadrats and a simple random sampling technique, plant height and diameter at breast height (DBH) were measured in situ with the GLOBE Observer application (The Globe Program, NASA-sponsored) and a measuring tape, respectively. The rate of canopy coverage over the past twenty years was assessed using historical Landsat 7 and 8 images available on Google Earth Pro. Rhizophora allometric equations were used to estimate the above-ground biomass (AGB), below-ground biomass (BGB), and total biomass (TB). Total organic carbon (TOC), sediment carbon storage, and all essential nutrients for plant growth were analysed using standard methods. Soil texture was determined using granulometric analysis. Our findings show that in both natural and planted forests, the zones that were more inundated were first established. However, tree and sapling density, as well as biomass, were negatively correlated with sodium (density: r = −0.830; AGB: r = −0.880). Positive correlations between AGB and nitrate nitrogen (NO3-N) (r = 0.686), ammonium nitrogen (NH4-N) (r = 0.706), phosphate (r = 0.618), and manganese (r = 0.776) suggest that these minerals were limiting factors. Nevertheless, the combination of forest age and salinity was found to play key roles on the AGB and therefore on total organic carbon (TOC), which is linked to materials originating from the mangroves. High correlations of TOC with NH4-N (r = 0.822) and magnesium (r = 0.831) indicate the greater availability of these essential nutrients in older forests. It is noteworthy that the Ferney forest with a relatively lower salinity (5–15 ppt) and the only forest that had already reached a steady state in 2010, had a relatively much higher AGB (326.2 ± 26.3 t ha−1) than the global average for Rhizophora mucronata (94.8 t ha−1), let alone Rhizophora spp. (281 t ha−1). The TOC registered at Ferney (47.34%) was also higher than the global values reported (2.00 ± 2.20% to 40.00 ± 2.20%).
Deep mining of megawatt large wind turbine actual operating data: exploration of Bayesian parameter fusion modeling
Authors: Wu Weimin , Liu Xiongfei , Ren Yu , Zhang Suocheng , Yan Wanjun , Du Wenqiang 
Published: January 2025

Abstract
Real-time operation data analysis and condition monitoring of large wind turbines are crucial for efficient and safe operation of wind farms. In response to this, an accurate prediction model architecture based on the multivariate linear regression algorithm is proposed for a deeper understanding of the actual operation of large wind turbines. Comparing different prediction variables, we have confirmed that the average wind direction and average wind speed play a core role in predicting active power. The display has significantly better approximation than the traditional multiple linear regression (MLR), especially in the peak region of period-like variation. The quasi-periodic peak variation characteristics of the active power near the observed series 2000, 2500 and 3000 are realized, and the peak prediction results of B-MLR are significantly better than those of traditional MLR. Meanwhile, for RMSE and R-squared, B-MLR is significantly better than the traditional MLR algorithm in the active power prediction of wind turbines. However, the data complexity in actual applications poses a challenge to the effectiveness of the Bayesian fusion algorithm. Further optimization of the algorithm is needed to cope with the complex and variable real data environment. This study provides scientific evidence for the efficient operation, precise maintenance, and environmentally friendly design of wind turbines, promoting the continuous progress and development of wind power generation technology.
Bridging theory and practice in peer-to-peer energy trading: market mechanisms and technological innovations
Authors: Raghoo Pravesh , Shah Kalim 
Published: January 2025

Abstract
The article provides a synthesis of the literature on the peer-to-peer (P2P) energy trading paradigm. P2P energy trading is a prosumer business model and a transformative concept that allows prosumers to sell surplus generation to other prosumers and consumers within an energy community or microgrid. P2P energy trading is a novel concept to promote decentralization, decarbonization, democratization, digitalization, and enhancing energy resilience of the energy sector. The article covers different facets of P2P energy trading, including market designs, changing actor roles, pricing mechanisms, enabling technologies, and challenges. The article thus addresses emerging and complementary aspects not covered in prior literature reviews. As such, three market designs are discussed: centralized, decentralized, and distributed, and four pricing mechanisms, which are optimization, game theory, auction-based, and reinforcement learning. Enabling technologies discussed are Energy Internet, Internet of Things, Artificial intelligence, Blockchain, Communication networks, and battery flexibility. The paper discusses the challenges that the development and commercialization of the P2P energy trading faces—especially focusing on the social ontology of the concept—and provides research directions to amplify the scaling up of the technology.
Learning Citizenship Matters
Authors: Shanahan Brendan A 
Published: January 2025

Abstract
This chapter examines the proliferation of state anti-alien professional licensure laws and hiring policies during the early to mid-twentieth century and explores legal, political, and administrative battles over their implementation. Unlike concurrent blue-collar nativist hiring measures, anti-alien professional licensure laws rarely became “dead letters” in practice when unemployment was low. Instead, state licensing board members, who generally had little expertise in immigration and citizenship law, frequently adopted harsh ad hoc documentation requirements and applied them unsparingly when East and South Asian immigrants, women, and refugees sought licensure. Immigrant professionals and their allies fiercely resisted these laws before judges and licensing board members. They also appealed to voters and policymakers for aid. But their efforts found limited success prior to the early Cold War. This chapter argues that the growing ubiquity and tortuous enforcement of nativist state licensure laws increasingly concretized citizenship rights as citizen-only rights in American professional employment.