bluuebunny/crossref_metadata_2025_split huggingface dataset:
eROSITA X-ray analysis of the PeVatron candidate Westerlund 1
Authors: Haubner Konstantin , Sasaki Manami , Mitchell Alison , Ponti Gabriele , Rowell Gavin , Einecke Sabrina , Filipović Miroslav , Lazarević Sanja , Pühlhofer Gerd , Strong Andrew 
Published: January 2025

Context. It is still unclear which fraction of cosmic rays above an energy of 1 PeV is accelerated by the observed Galactic PeVatron population. These sources of unknown physical origin are detected through their γ-ray emission, which also identifies them as particle accelerators. However, their γ-ray data are typically degenerate between hadronic and leptonic emission scenarios, which hinders their straightforward association with the mainly hadronic cosmic ray population.
Aims. In this study, we aimed to distinguish between leptonic and hadronic particle acceleration scenarios for the PeVatron candidate HESS J1646−458, which is associated with the star cluster Westerlund 1 (Wd 1). To this end, we first studied the diffuse X-ray emission from Wd 1 to better understand if its origin is of thermal or nonthermal nature. In addition, we searched for X-ray synchrotron emission from the associated PeVatron candidate HESS J1646−458 to put new constraints on the magnetic field strength and the leptonic particle population of this source.
Methods. We used data from the all-sky surveys 1 to 4 of the extended Roentgen Survey with an Imaging Telescope Array (eROSITA) on board the Spectrum-Roentgen-Gamma orbital platform to spectrally analyze the diffuse emission from Wd 1 and HESS J1646−458. For Wd 1, we fitted and compared a purely thermal model and a model with a thermal and a nonthermal component. Next, we analyzed the spectra of four annuli around Wd 1 that coincide with HESS J1646−458 to search for synchrotron radiation.
Results. We find that eROSITA data cannot distinguish between thermal and nonthermal source scenarios for the diffuse emission from Wd 1 itself. For a thermal source scenario, the observed X-ray flux can be explained in large part by unresolved pre-main sequence stars or by thermalized stellar wind shocks. In the case of the PeVatron candidate HESS J1646−458, we find no evidence of synchrotron emission. We estimated an upper confidence bound of the synchrotron flux up to 40 ′ around Wd 1 of 1.9  ⋅  10−3 keV−1 cm−2 s−1. We used this result to study the spectral energy distribution of the source. From that, we obtained an upper 1σ confidence bound on the magnetic field strength of HESS J1646−458 of 7 μG.
Conclusions. Our upper bound on the magnetic field strength in HESS J1646−458 is compatible with a previous estimate in the literature for a fully leptonic source scenario. Therefore, a purely leptonic emission scenario is compatible with our results. The same is the case for hadronic and hybrid scenarios, for which even less synchrotron flux is expected compared to the leptonic scenario.
Fast Bayesian spectral analysis using convolutional neural networks: Applications to GONG Hα solar data
Authors: Castelló G. , Luna M. , Terradas J. 
Published: January 2025

Context. Solar filament oscillations have been observed for many years, but recent advances in telescope capabilities now enable a daily monitoring of these periodic motions. This offers valuable insights into the structure of filaments. A systematic study of filament oscillations over the solar cycle can shed light on the evolution of the prominences. Only manual techniques were used so far to analyze these oscillations.
Aims. This work serves as a proof of concept and demonstrates the effectiveness of convolutional neural networks (CNNs). These networks automatically detect filament oscillations by applying a power-spectrum analysis to Hα data from the GONG telescope network.
Methods. The proposed technique studies periodic fluctuations in every pixel of the Hα data cubes. Using the Lomb-Scargle periodogram, we computed the power spectral density (PSD) of the dataset. The background noise fits a combination of red and white noise well. Using Bayesian statistics and Markov chain Monte Carlo (MCMC) algorithms, we fit the spectra and determined the confidence threshold of a given percentage to search for real oscillations. We built two CNN models to obtain the same results as with the MCMC approach.
Results. We applied the CNN models to some observations reported in the literature to prove its reliability in detecting the same events as the classical methods. A day with events that were not previously reported was studied to determine the model capabilities beyond a controlled dataset that we can check with previous reports.
Conclusions. CNNs prove to be a useful tool for studying solar filament oscillations using spectral techniques. The computing times are significantly reduced for results that are similar enough to the classical methods. This is a relevant step toward the automatic detection of filament oscillations.
Relating Sustainability Metrics to Evaluate Circularity and Material Efficiency
Authors: Marshalek Austin , Zion Andie , Silverman Julian R. 
Published: January 2025

AbstractBy collecting both green chemistry and environmental impact metrics, researchers can quantitatively discuss the circularity and sustainability of various chemical and material products and processes. Specific extensive quantities from a transformation are related to afford intensive properties for a system, which can be compared across systems. Because different metrics are suited for discussing the diverse aspects of sourcing, transformation, and disposal of reactants, reagents, products, and waste, to meaningfully describe both circularity and sustainability, at least three metrics are needed. Here we compare, contrast, and relate the intensive metrics of recycled content, process mass intensity, and e-factor to describe the renewable content of feedstocks, the efficiency of a process, and the cycling of outputs as input feedstocks. Using data from mass balances, we can calculate these metrics in order to have a simple and concise set of tools that describe both the circularity and material efficiency of a process.
Reforming Doctoral Education in Uzbekistan: Models, Rules and Norms of Behaviour
Authors: Ruziev Kobil , Rabbimova Dilshoda , Mamasolieva Mokhidil 
Published: January 2025

Abstract
Doctoral education (DE) plays a crucial role in nurturing the next generation of scholars and thought leaders and serves as a means for frontier knowledge creation and innovation within the economy. The centrality of DE to knowledge creation and skilled workforce development makes it a policy priority for governments worldwide. While emerging economies like Uzbekistan seek to replicate aspects of successful DE models from developed countries, their policy borrowing approach often overlooks important national conditions. Instead of improving DE outcomes, the recent DE reforms in Uzbekistan resulted in the proliferation of unfair practices. This study examines these reforms using relevant national regulatory documents, secondary data, and primary data collected through semi-structured interviews. Drawing on institutional economics as our theoretical framework, we explore how incentive mechanisms, rules, norms of behaviour, and capacity constraints interact, explaining the persistent mismatch between policy goals and policy outcomes. We argue that effective DE reforms require not only well-defined rules but also credible enforcement mechanisms and supportive norms of behaviour. We highlight the need for reforms to address incentive structures, building trust in institutions, and overcoming path dependence challenges to sustainably improve DE outcomes.
Self-translation by an academic in exile
Authors: Phanthaphoommee Narongdej 
Published: January 2025

Abstract
In the wake of the twelfth successful coup in 2014, Thailand began to crack down on academics critical of the
junta’s authority. Many fled to other countries but continue to be politically active, both online and offline. This study
examines the case of Pavin Chachavalpongpun, an exiled political scientist and outspoken opponent of the military coup, and his
self-translated academic book condemning the illegitimate seizure of power. His original work, A Plastic Nation,
and its Thai translation, ชาติพลาสติก Chat Plastic, are closely analysed in terms of intention and intertext.
The self-translator has used quotation marks to emphasise key words, adapted terms to reflect local and international concepts,
rewrote sections to make new points, and broke with centuries-old norms when mentioning the monarchs in Thai spoken and written
discourse. Paratexts also play a crucial role in conveying the ideological stance of the publisher and self-translator towards the
military government. They serve as a deliberate reflection in which forms of contention are regenerated with political aspirations
to expose the exploitation of Thai nationalism by those in power and to protest the unconstitutional military takeover that sought
to preserve the authoritarian legacy. Pavin’s self-translation takes on a new meaning. It could shed some light on what
self-translation as academic activism ‘can do’, in addition to what self-translation is.
Smearing out contact terms in ghost-free infinite derivative quantum gravity
Authors: Beckering Vinckers Ulrich K , de la Cruz-Dombriz Álvaro , Mazumdar Anupam 
Published: January 2025

Abstract
In the context of ghost-free infinite derivative gravity, we consider the single graviton exchange between two spinless particles, a spinless particle and a photon, or between a spinless particle and a spin-1/2 particle. To this end, we compute the gravitational potential for the three aforementioned cases and derive the

O

(
G

ℏ
2

)

correction that arises at the linearised level. In the local theory, it is well-known that such a correction appears in the form of a Dirac delta function. Here, we show that this correction is smeared out for the nonlocal theory and, in contrast to the local theory, takes on non-zero values for a non-zero separation between the two particles. In the case of the single graviton exchange between a spinless particle and a spin-1/2 particle, we also compute the

O

(
G
ℏ
)

correction that arises in the non-static case within the non-relativistic approximation and show that it is finite in the nonlocal theory.
Analytic weak-signal approximation of the Bayes factor for continuous gravitational waves
Authors: Prix Reinhard 
Published: January 2025

Abstract
We generalize the targeted

B

-statistic for continuous gravitational waves by modeling the h
0-prior as a half-Gaussian distribution with scale parameter H. This approach retains analytic tractability for two of the four amplitude marginalization integrals and recovers the standard

B

-statistic in the strong-signal limit (

H
→
∞

). By Taylor-expanding the weak-signal regime (

H
→
0

), the new prior enables fully analytic amplitude marginalization, resulting in a simple, explicit statistic that is as computationally efficient as the maximum-likelihood

F

-statistic, but significantly more robust. Numerical tests show that for day-long coherent searches, the weak-signal Bayes factor achieves sensitivities comparable to the

F

-statistic, though marginally lower than the standard

B

-statistic (and the Bero-Whelan approximation). In semi-coherent searches over short (compared to a day) segments, this approximation matches or outperforms the weighted dominant-response

F

^

ABw

-statistic and returns to the sensitivity of the (weighted)

F

^

w

-statistic for longer segments. Overall the new Bayes-factor approximation demonstrates state-of-the-art or improved sensitivity across a wide range of segment lengths we tested (from 900 s to 10 days).
Producing micro impressions on Al6061 under alumina-mixed deionized water as dielectric during electric discharge machining
Authors: Asad Muhammad , Sana Muhammad , Farooq Muhammad Umar , Tlija Mehdi 
Published: January 2025

Abstract
The need for materials with low density and high strength has drawn a lot of interest from researchers and industry in the last few years. Aluminum 6061 (Al6061) is one of these materials that has the required qualities. Powder-mixed electric discharge machining has become a practical choice for cutting such materials because of its versatile machining capabilities. However, this technique’s excessive energy usage and poor cutting efficiency have drawn criticism. Furthermore, there are serious health and environmental risks associated with the typical dielectric (kerosene) used in EDM. Deionized water, a replacement to kerosene, has been used in this work to address the aforementioned problems, improving resource reusability and lowering the dielectric cost. Here, deionized water further makes the operation sustainable and protects the environment from harmful emissions produced during the machining process. Additionally, alumina (Al2O3) nano-powder has been mixed in dielectric and used to improve the machining responsiveness. Response surface methodology was used to carry out the investigation. The purpose of this study was to use microscopic analysis to examine the effects on the electrode wear rate (EWR) and accuracy index (AI). Analysis of variance (ANOVA) analyses for both responses revealed that all four parameters are highly significant, with p-values nearly zero (<0.05). Additionally, the coefficient of determination (R
2) values for EWR (0.9611) and AI (0.9285) indicate that the proposed models are reliable. The parametric optimization by grey relational analysis (GRA) approach highlighted that the magnitude for EWR and AI is improved by 50.85% and 2.67%, respectively, when optimal condition (I
P: 5 A, S
V: 2 V, S
T: 3 µs, and C
P:1.5 g/100 ml) is set during EDM of Al6061. The proposed EDM model yielded 48.29% and 5.11% better outcomes than the conventional EDM model in terms of EWR and AI, respectively.
GHz SAW strain sensors under high mechanical load conditions
Authors: Kwon MinHee , Schlögl Matthias , Platz Daniel , Schmid Ulrich 
Published: January 2025

Abstract
Strain sensors have been developed in various fields by converting mechanical deformation into electrical signals. Surface acoustic wave (SAW) devices are beneficial for strain sensing due to their simplicity of fabrication and wireless operation capabilities. In this study, we investigate SAW strain sensors operating at 1.25

GHz

. The fabricated SAW resonators using standard photolithography technology are characterized with a custom-made cantilever setup capable of applying defined strain values up to approximately −4000 µε to 4000 µε. From these measurements, a high responsivity even up to this high strain values is demonstrated. We also explore the impact of geometric design parameters on strain-sensing performance. We vary the length of the SAW resonator and observe that the longer the SAW resonator, the more responsive the device gets to strain changes. When the distance between the two reflectors confining the SAW is 2207

μ

m

, the responsivity to strain is 114.99

Hz

μ

ϵ

−
1

. In summary, this study investigates the feasibility of GHz SAW resonators as high-strain sensors on non-flexible substrates with a custom-built experimental setup, to evaluate their potential for future applications in extreme mechanical environments.
Spectral characterization method for wire-arc additive manufacturing monitoring with broadband AE signals
Authors: Gao Fei , Li Congyu , Ji Dingcheng , Hua Jiadong , Lin Jing 
Published: January 2025

Abstract
Wire-arc additive manufacturing (WAAM) is acknowledged as a highly effective and economical 3D printing method for large component production and repair. However, defects like porosity, oxidization, and distortion may occur due to the high deposition rate and improper manufacturing process, leading to a vital concern about the structural quality. Acoustic emission (AE) signals generated with the WAAM process carry a wealth of manufacturing information, but the interpretation of AE signals often requires a deep understanding of both the physics of the AM process and the characteristics of the AE signal. This interdisciplinary knowledge gap can hinder the development of accurate and reliable signal analysis models. To reveal the manufacturing physics-related AE features from the complicated broadband AE signals, a spectral decomposition and characterization method is proposed and investigated for WAAM process monitoring. The AE hits are first identified with the average filtering and envelope peak finding so that the intense noise can be suppressed and AE hits can be highlighted. Secondly, the wavelet entropy and spectral centroid are extracted for each AE hit to characterize the printing status with the AE waveform. Then, the AE features varying with the manually controlled printing status are investigated and fused with experimental study, thus improving the identification accuracy. The experimental investigation on four coupons manufactured with shielding gas flow control shows that the spectral features are sensitive to the WAAM process, thereby providing a promising candidate for real-time monitoring and evaluation of WAAM.
YOLOv8-SC: improving the YOLOv8 network for real-time detection of automotive coated surface defects
Authors: Ling Lin , Zhu Chuangman , Liu Mingzhou , Hu Jing , Zhang Xi , Ge Maogen 
Published: January 2025

Abstract
The detection of defects on automotive coated surfaces is of paramount importance to ensure the quality of automotive appearance. However, due to the challenges posed by the lack of sufficient samples of product-specific coated defects, the uneven number of species, and the difficulty of detecting defects in small targets due to the complex background of the body surface. The detection of defects on automotive coated surfaces still relies on experienced manual labor to a significant extent. To address these issues, Poisson fusion is employed to enhance the data for generating defect images, thereby mitigating the limitations of insufficient coated defect samples and an imbalanced distribution of defect species. Based on this, a new YOLOv8-SC defect detection model is proposed, in which the C2f-Star is used to replace the original C2f structure. This aims to improve the model’s ability to extract defects of varying sizes in complex backgrounds, while reducing the number of model parameters and the amount of computation. The content-guided attention fusion (CGAFusion) module, situated before each detection head of the model, has been designed to enhance the model’s capacity to extract defects of varying sizes and complexity. This is achieved through the adaptive fusion of low-level and high-level features. The proposed model’s superiority in terms of detection metrics has been demonstrated through the use of example validation. The experiments were conducted on the enterprise dataset CPD-DET and the public dataset NEU-DET. The results demonstrated that the defective image generation and data enhancement method could significantly improve detection performance and have good generalization. The proposed YOLOv8-SC reduces the model parameters by 12.2% compared to the normal model. Additionally, the mean average precision (mAP) at 50% and 50%–95% thresholds are enhanced by 4.7% and 2.3%, respectively. Furthermore, the accuracy of the model exhibits a discernible positive growth trend with an increase in the sample set size. The design of an automotive coated surface defect detection system is presented at last. When deployed in industrial settings, this system can facilitate the intelligent detection of coated defects.
CEEMDAN and adaptive distance embedding for fault diagnosis of train bogie bearing
Authors: Li Jiayi , He Deqiang , Wei Zexian , Zhao Ming , Xiang Zaiyu 
Published: January 2025

Abstract
As an essential part of the train, the health status of the bogie bearing is vital to its safety. In a complex working environment, the fault signal of the bearing is highly dimensional and nonlinear, which makes high-precision fault diagnosis difficult. Therefore, a fault diagnosis method based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and adaptive weighted distance manifold learning (AWDU) is to address this issue. Firstly, an intrinsic modal component screening index is presented, which takes into account the advantages of the cross-correlation coefficient, mean square error, and kurtosis coefficient. It is embedded in CEEMDAN to improve further the algorithm’s signal decomposition and noise reduction ability. Secondly, the AWDU technique is proposed to reduce the dimensionality of raw data, which can accurately calculate the distance and adjacency relationship between high-dimensional data and better guide low-dimensional embedding. Eventually, a range of experiments was executed across two different benchmark cases, and the results indicated that the average accuracy of this method in each experiment was not less than 99.64%, and the performance was satisfactory in the comparison of similar methods, which verified the effectiveness, stability, and generalization of the proposed method.
Fault diagnosis in rotation motor bearings: novel interpretable indicator and improved snow ablation optimizer
Authors: Bai Xusong , Song Xiangjin , Wang Zhaowei , Chen Qian , Zhao Wenxiang , Fan Wei 
Published: January 2025

Abstract
Early bearing fault diagnosis is essential for stable operation and low-cost maintenance of rotation motors. A rolling bearing fault diagnosis method for rotation motors is proposed, leveraging a novel interpretable short-time kurtosis error (SKE) indicator and the improved snow ablation optimizer (ISAO). Initially, the Cauchy mutation mechanism is employed to bolster the global search capability of ISAO, thereby optimizing parameter settings. Subsequently, ISAO is utilized to adaptively configure key parameters within the adaptive feature mode decomposition (AFMD), reducing reliance on prior knowledge. The SKE indicator is then developed to enhance the efficacy of AFMD in extracting fault features. The proposed AFMD is validated using bearing single and composite fault data from two experimental platforms. The experimental results under various fault conditions show that AFMD can adaptively perform fault feature extraction and robust to noise interference. Meanwhile, the proposed method performs better than existing methods, such as traditional FMD and dung beetle optimizer-based variational mode decomposition. This study advances state-of-the-art bearing fault diagnosis and offers a more interpretable and adaptive rotation motor condition monitoring solution.
Robust and general reading recognition for pointer meters based on key point detection and large language model
Authors: Zhang Wenbiao , Li Peizhe , Bai Xiaojing 
Published: January 2025

Abstract
To meet the requirements of smart factories and intelligent industrial settings, it is of great significance to develop a reading recognition method for pointer meters. As existing methods lack the robustness necessary for complex environments and are overly specialized for specific meter types. In this paper, a robust and general method for pointer meter reading recognition is introduced. Our method demonstrates adaptability to a wide range of pointer meter types and maintains effectiveness under extreme conditions. Initially, a Tiny-YOLO object detection network is utilized to distinguish meters from the background. Subsequently, a multi-head key point detection network with a fusion strategy based on Swin Transformer is employed to identify four key points. The unique multi-head structure facilitates the key point detection of various image quality. The percentage indication is calculated using the angular method based on the key points. The numerical indication and the units come from the refined optical character recognition network by a large language model. Several extreme experiments have been conducted to demonstrate the robustness of our methods, with an average quoted error of less than 0.24%.