bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self-supervised learning for machinery fault diagnosis under strong background noise and limited epochs
Authors: Chen Mingsong , Wang Hongwei , Zhang Fanghong 
Published: January 2025

Abstract
Self-supervised learning (SSL) has been widely used for fault diagnosis of rotating machinery. However, real industrial environments often generate a large amount of noise, and existing SSL-based fault diagnosis methods are, firstly, difficult to apply to fault diagnosis under highly noisy working conditions due to the inherent scarcity of labeled samples. Secondly, most of the existing methods add projection head during pre-training and classification head during fine-tuning, such a design makes the number of parameters and computations of the model excessive, which requires a lot of time for training and cannot meet the needs of low-end edge device deployment. To solve the above problems, this paper proposes a multi-scale channel-mixed depthwise separable convolutional neural network-based fault diagnosis method under SSL. Experimental results on bearing datasets show that the proposed method has a significant advantage over other methods in terms of strong noise fault diagnosis accuracy under limited labeled samples, and also the number of parameters and computational cost of the model are minimized. Specifically, with only 10 pre-training epochs and 50 fine-tuning epochs under 5% labeled data, the proposed method can achieve up to 96.35% average accuracy on the PU dataset with strong noise interference.
Investigation of ion temperature in low-density undercritical foams
Authors: Hudec L , Limpouch J , Renner O , Tikhonchuk V , Dudžák R , Liska R , Blackman D R , Condamine F , Červeňák M , Dostál J , Gintrand A , Klimo O , Krupka M , Krůs M , Singh S , Weber S 
Published: January 2025

Abstract
The ion temperature in laser-heated foam materials can be considerably higher than the electron temperature due to the internal collisions of the plasma flows originating from the heterogeneous foam microstructure. Recently, we have developed a novel hybrid multiscale model for laser-foam interaction that successfully reproduces the experimentally measured heat front propagation in laser-heated subcritical foams of various densities. However, when applied to undercritical foams with average density closer to critical, the hybrid model simulations predict an ion–electron temperature ratio much larger than in any previously reported measurements and suggest that the influence of foam microstructure is more impactful for larger average densities. For such foams, the laser-driven heat front velocity was measured many times, but the ion temperature received much less attention. To investigate the ion temperature, the laser interaction with 10 mg

cm

−
3

undercritical chlorine-doped TMPTA foams has been studied at the PALS facility, using an extended diagnostic complex emphasizing the x-ray time-resolved studies of the plasma wave propagation inside the foam and the distribution of macroscopic plasma parameters via high-resolution x-ray spectroscopy. The ion and electron temperatures have been measured from Doppler broadening and the relative intensity ratio of chlorine x-ray spectral lines. The averaged ion and electron temperature ratio ranges from 2 to 4 depending on the laser pulse energy. The simulations agree reasonably well with the experimental results.
Simulation of ion cyclotron range of frequencies heating in the proton–boron plasma of the spherical tokamak
Authors: Ma Hao-jie , Xie Hua-sheng , Sun Guang-lan , Zhao Han-yue , Gan Yan-biao 
Published: January 2025

Abstract
Ion cyclotron range of frequencies (ICRF) heating is crucial in magnetic confinement fusion devices that utilize proton–boron11 (p11B) as an advanced fuel, as it requires higher ion temperatures for their reactions. This work investigates the ICRF heating characteristics of hydrogen (H) ions at the second harmonic frequency and 11B ions at the fundamental frequency in a p11B plasma within a spherical tokamak. It is found that H ions second harmonic is better than 11B ions fundamental for ion power absorption. We simulate the ICRF heating effects of the 11B fraction in plasma and the wave vector parallel to the magnetic field (

k

‖

) of the antenna using full-wave codes and the kinetic dispersion relation solver. In second harmonic heating of H ions, the power deposition primarily occurs on the H ions themselves, and, the total power absorption will exceed 50%, up to 58%, when X[11B] > 6%. Heating the electrons is easier in the fundamental frequency heating of 11B ions scenario.
Bayesian method in optical emission spectroscopy: temporal evolution of electron density from time-integrated Hα emission and validation with time-resolved measurements for pulsed nanosecond discharges in water
Authors: Dorval Audren , Hamdan Ahmad , Stafford Luc 
Published: January 2025

Abstract
The characterization of in-liquid discharges is known to be a challenging feat due to their stochastic nature and nanosecond time scale evolution. In this study, the time-resolved electron density (n
e) of a spark discharge in water is analyzed by coupling optical emission spectroscopy (OES) measurements with a Bayesian model. It is first highlighted that a single Voigt profile cannot adequately describe the time-averaged Hα line profile; this is due to the significant time evolution of the discharge properties. To overcome this limitation, a model describing the temporal evolution of the line emission intensity and shape is developed and used to fit the time-integrated spectrum. The unknown parameters in the model are determined using the Dynesty python package, according to the Bayesian nested sampling method. With such model, the simulated and measured spectrum of the Hα transition agree very well. Over the range of experimental conditions investigated, it is found that the electron density rapidly reaches

∼

2
×

10

25

m

−
3

and then decreases exponentially with a decay time of

∼

238

ns

. These values are consistent with those determined using time-resolved measurements and analysis of the Hα and O I line broadenings. Overall, this study shows that time-resolved plasma properties can be obtained from time-integrated OES data by applying a Bayesian-based modeling approach. Further studies are needed to expand the scope of the developed model and determine plasma properties over a broad range of conditions.
A cordial introduction to double scaled SYK
Authors: Berkooz Micha , Mamroud Ohad 
Published: January 2025

Abstract
We review recent progress regarding the double scaled Sachdev–Ye–Kitaev model and other p-local quantum mechanical random Hamiltonians. These models exhibit an expansion using chord diagrams, which can be solved by combinatorial methods. We describe exact results in these models, including their spectrum, correlation functions, and Lyapunov exponent. In a certain limit, these techniques manifest the relation to the Schwarzian quantum mechanics, a theory of quantum gravity in AdS
2. More generally, the theory is controlled by a rigid algebraic structure of a quantum group, suggesting a theory of quantum gravity on non-commutative q-deformed AdS
2. We conclude with discussion of related universality classes, and survey some of the current research directions.
On the Response of Massive Main Sequence Stars to Mass Accretion and Outflow at High Rates
Authors: Bear Ealeal , Soker Noam 
Published: January 2025

Abstract
With a one-dimensional stellar evolution model, we find that massive main sequence stars can accrete mass at very high mass accretion rates without expanding much if they lose a significant fraction of this mass from their outer layers simultaneously with mass accretion. We assume the accretion process is via an accretion disk that launches powerful jets from its inner zones. These jets remove the outer high-entropy layers of the mass-accreting star. This process operates in a negative feedback cycle, as the jets remove more envelope mass when the star expands. With the one-dimensional model, we mimic the mass removal by jets by alternating mass addition and mass removal phases. For the simulated models of 30M
⊙ and 60M
⊙, the star does not expand much if we remove more than about half of the added mass in not-too-short episodes. This holds even if we deposit the energy the jets do not carry into the envelope. As the star does not expand much, its gravitational potential well stays deep, and the jets are energetic. These results are relevant to bright transient events of binary systems powered by accretion and the launching of jets, e.g., intermediate luminosity optical transients, including some luminous red novae, the grazing envelope evolution, and the 1837–1856 Great Eruption of Eta Carinae.
Mutual exclusion statistics and the infinite-range Hubbard model: critical behavior in the quantum regime
Authors: Ramos G F O , Rosendo W L S , Raposo E P , Coutinho-Filho M D 
Published: January 2025

Abstract
We investigate the critical behavior in the quantum regime of the Hubbard model in d-dimensional hypercubic lattices with infinite-range Coulomb interaction. The Haldane–Wu mutual exclusion statistics approach, which comprises three species of exclusions, allows us to derive the singular part of the free energy, entropy, and specific heat in the quantum regime g > T, where g is the scaling interaction parameter and T is the temperature. We present results from the analytic series expansions of observables near the quantum critical point:

T
=
g
=
μ
=
0

, where µ is the chemical potential. The signature of the mutual exclusion statistics manifests through the appearance of polylogarithms related to Lerch functions in the coefficients of the free energy series in powers of

T

/

g

. The calculated critical exponents from the analytic series are consistent with our scaling analysis in the quantum regime. In particular, the specific heat is shown to display a variety of critical behaviors,

C

/

T
∼

g

−

α
g

,

g
→

0
+

, depending on the lattice dimension: from a singular behavior in d = 1 (

α
g

=
1

/

2

) to a constant value in d = 2 (

α
g

=
0

), and regular behavior for d > 2 (

α
g

=
1
−
d

/

2
<
0

).
Lateral flow immunoassay combined with color saturation for smartphone-based quantitative point-of-care detection of Klebsiella pneumoniae
Authors: Pham Thu Thao , Huyen Nguyen T T , Tran Lam Dai , Lien Truong T N 
Published: January 2025

Abstract

Klebsiella pneumoniae is one of the most common pathogenic, multidrug-resistant Gram-negative bacteria that triggers postoperative infections, leading to septic shock and fatality. Therefore, there is an urgent need to generate rapid and sensitive diagnosis techniques for timely and effective intervention. The present work described the fabrication of lateral flow immunoassays (LFIA) based on antibody-gold nanoparticle covalent conjugation combined with an RGB system to detect Klebsiella pneumoniae quantitatively. The LFIAs showed the lines after 15 min with good sensitivity and a low limit of detection of 2.40 CFUs ml−1 in the linear detection range of this immunosensor from 101 to 108 CFUs ml−1 when combined with the color saturation system. No false positives and cross-reactions were observed on the test strips in this study, indicating that the system was optimized with high specificity. This finding might provide a well-suited solution for a quick, inexpensive, and easy-to-use detection method for potential commercial manufacture.
Microstructural transformation of GO to rGO by ascorbate acid green-agent enhanced using sonication method
Authors: Masruroh  , Sukma Freygieon Ogiek Rizal , Saputra Kormil , Mufti Nandang , Kusumandari  , Prasetyo Anton , Tjahjanto Rachmat T , D H Santjojo Dionysius J 
Published: January 2025

Abstract
Reduced graphene oxide (rGO) was successfully synthesized from graphene oxide (GO) using ascorbic acid as a reducing agent, combined with a sonication process to improve microstructural transformation and enhance optical properties. The primary objectives of this study were to evaluate the role of ascorbic acid in altering the microstructure of rGO and to examine the effect of sonication duration on the band gap. Characterization of particle size distribution and band gap was carried out using PSA and UV–vis, while changes in microstructure, crystallization, and morphology were analyzed using XRD, Raman spectroscopy, and SEM/EDX. The results showed that increasing the sonication duration decreased the particle size of rGO significantly, contributing to microstructural transformation. The decrease in the bandgap of rGO was also confirmed through UV–vis characterization. FTIR analysis showed a decrease in the absorption intensity of the O=C=O (2200–2450 cm−1), C–O (1024–1150 cm−1), and O–H (3650–3800 cm−1) peaks, indicating a decrease in oxygen groups. SEM showed a change in the morphology of the compact GO surface to a looser and folded rGO, with a decrease in the oxygen ratio detected by elemental mapping. The increase in the intensity of the D band in the Raman spectrum indicated an increase in structural defects due to the reduction of oxygen groups. Crystallite size calculations from Raman and XRD revealed that rGO had a smaller crystallite size than GO, where rGO with a sonication duration of 120 min had a smaller crystallite size than rGO with 180 min, indicating an expansion of the sp2 domain. The combination of ascorbic acid and sonication proved effective in reducing GO to rGO with significant improvements in material characteristics.
GradeDiff-IM: an ensembles model-based grade classification of breast cancer
Authors: Manna Sweta , Mistry Sujoy , Dahal Keshav 
Published: January 2025

Abstract
Cancer grade classification is a challenging task identified from the cell structure of healthy and abnormal tissues. The practitioners learns about the malignant cell through the grading and plans the treatment strategy accordingly. A major portion of researchers used DL models for grade classification. However, the behavior of DL models is hidden type, it is unknown which features contribute to the accuracy and how the features are chosen for grading. To address the issue the study proposes a Grade Differentiation Integrated Model (GradeDiff-IM) to classify the grades G1, G2, and G3. In GradeDiff-IM, different ML models, are used for grade classification from clinical and pathological reports. The biological-significant features with ranking technique prioritize influential features are used to identify grades G. Subsequently, histopathological images are used by DL models for grade classification and compared with ML models. Instead of employing a single ML model, the GradeDiff-IM model uses the stack-ensembled approach to improve the grade G classification performance. The maximum accuracy is attained by stacking G1-98.2, G2-97.6, and G3-97.5. The proposed study shows that the ML ensemble model is more accurate than the DL models. As a result, the proposed model achieved higher accuracy for G by implementing the stacking technique than the other state-of-the-art models.
Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis
Authors: Wu Guanghui , Li Xiang , Xu Yunfeng , Wei Benzheng 
Published: January 2025

Abstract
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing brain imaging data, ignoring other non-imaging data (e.g., genetic, clinical information, etc.) that may have underlying disease information. In addition, imaging data acquired from different devices may exhibit varying degrees of heterogeneity, potentially resulting in numerous noisy connections during network construction. To address these challenges, this study proposes a Multimodal Multiview Bilinear Graph Convolution (MMBGCN) framework for disease risk prediction. Firstly, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) features are extracted from magnetic resonance imaging (MRI), and non-imaging information is combined with the features extracted from MRI to construct a multimodal shared adjacency matrix. The shared adjacency matrix is then used to construct the multiview network so that the effect of potential disease information in the non-imaging information on the model can be considered. Finally, the MRI features extracted by the multiview network are weighted to reduce noise, and then the spatial pattern is restored by bilinear convolution. The features of the recovered spatial patterns are then combined with the genetic information for disease prediction. The proposed method is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Extensive experiments demonstrate the superior performance of the proposed framework and its ability to outperform other related algorithms. The average classification accuracy in the binary classification task in this study is 89.6%. The experimental results demonstrate that the method proposed in this study facilitates research on MCI diagnosis using multimodal data.
Fully integrated wearable control system for micro/nanorobot navigation
Authors: Zhang Zhanxiang , Wang Lin , Jiang Fengqi , Yu Shimin , Ji Fengtong , Sun Tianhao , Zhang He , Zhu Yanhe , Chang Hao , Li Tianlong , Zhao Jie 
Published: January 2025

Abstract
Micro/nanorobots have exhibited excellent application potential in the biomedical field, such as drug delivery, minimally invasive surgery, and bio-sensing. Furthermore, in order to achieve practical application, it is essential for swimming micro/nanorobots to navigate towards specific targets or adjust their speed and morphology in complete environments. The navigation of swimming micro/nanorobots with temporal and spatial precision is critical for fulfilling the demand of applications. Here, we introduced a fully integrated wearable control system for micro/nanorobots navigation and manipulation, which is composed of a multifunctional sensor array, an artificial intelligence (AI) planner, and a magnetic field generator. The sensor array could perceive real-time changes in gestures, wrist rotation, and acoustic signals. AI planner based on machine learning offers adaptive path planning in response to dynamically changing signals to generate magnetic fields for the on-demand manipulation of micro/nanorobots. Such a novel, feasible control strategy was validated in the biological experiment in which cancer cells were targeted and killed by photothermal therapy using micro/nanorobots and integrated control platform. This wearable control system could play a crucial role in future intelligent medical applications and could be easily reconfigured toward other medical robots’ control.
South Atlantic subtropical anticyclone responses to stratospheric aerosol injection
Authors: Chile Baldoni Thales , Reboita Michelle Simões , Machado Crespo Natália , Ribeiro João Gabriel Martins , da Rocha Rosmeri Porfírio 
Published: January 2025

Abstract
The South Atlantic Subtropical Anticyclone (SASA) is a key component of large-scale atmospheric circulation and is responsible for driving the climate in eastern Brazil and western Africa. Climate projections under warming scenarios suggest a strengthening, as well as a westward and southward expansion of this system. However, little is known about how the combination of global warming and climate intervention affects this system. To address this, SASA was identified from 2015 to 2099 in a set of projections with and without stratospheric aerosol injection (SAI). Projections were obtained from different initiatives: the Assessing Responses and Impacts of Solar Climate Intervention on the Earth System with SAI using Community Earth System Model version 2 (CESM2) global climate model, the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) using CESM1, and the Geoengineering Model Intercomparison Project (GeoMIP/G6sulfur) using Max Planck Institute Earth System Model. As each project has its own specific model, scenario, SAI location, etc, no intercomparison was carried out among them. Instead, there is an indication of what occurs in each project when comparing the near (2040–2059) and the far future (2080–2099) projections under SAI and no-SAI scenarios. SASA under no-SAI scenarios, compared to the reference period (2015–2024), follows the pattern described in the literature, i.e. a tendency to be stronger and wider. However, these features are more evident in the GLENS project. This same project suggests that SAI scenarios contribute to reducing the impact of global warming on the SASA climatology, as SASA in the future acquires characteristics similar to those of the reference period. One of the possibilities for it is that GLENS has the largest SAI forcing, given that the goal was to cancel out the strong greenhouse gas-induced warming in Representative Concentration Pathway 8.5.
Attributing heatwave-related mortality to climate change: a case study of the 2009 Victorian heatwave in Australia
Authors: Perkins-Kirkpatrick Sarah E , Selvey Linda , Aglas-Leitner Philipp , Lansbury Nina , Hundessa Samuel , Stone Dáithí , Ebi Kristie L , Osborne Nicholas John 
Published: January 2025

Abstract
Determining the influence of climate change behind human mortality is of interest to many sectors. However, it is a fledgling field where studies have centered on northern hemisphere events. This study presents the first attribution assessment on the mortality burden of an Australian heatwave to climate change. We focus on excess heatwave- (defined by climatological definitions) related mortality in the state of Victoria that occurred during the 2009 southeast Australian heatwave. An epidemiological model derived from well-established methods defining the relationship between observed heatwave temperatures (95th, 97.5th and 99th percentiles) and mortality is applied to heatwaves in simulations that either include or omit anthropogenic climate forcing from eight climate models. Across all models, the frequency of a heatwave-related mortality event similar to the 2009 Victorian event has, on average, doubled under factual conditions relative to counterfactual conditions. Moreover, on average, around 6 ± 3–4 extra individuals out of 31 (an increase of 20%) died as a direct result of extreme temperatures due to anthropogenic influence on the climate. Despite the small total number of attributable deaths as per the epidemiological model, six out of eight climate models predicted a statistically significant anthropogenic influence, indicating that climate change increased the heatwave-related mortality impact of this event. We make clear that, in line with previous Australian-based studies, the focus on mortality relative to the top 5% of temperatures logically infers a smaller mortality signal relative to the top 50% of temperatures, as would be defined by a more general temperature-related epidemiological model. As research, planning and policy interest in the role of climate change behind the burden health—and other adverse impacts of weather and climate extremes—continues to grow, it is vital that interdisciplinary collaborations are nurtured, so that the resulting science is of high-quality rigour, and policy relevance.