bluuebunny/crossref_metadata_2025_split huggingface dataset:
Atomic scale smoothing of nanoscale quartz mold using amorphous carbon films
Authors: Farghali Abdelrahman , Iwasa Kazutoki , Kim Jongduk , Choi Junho 
Published: July 2024

Abstract
Surface roughness control of end products is increasingly becoming significant, especially with the miniaturization trends in the semiconductor industry. Ultra-thin amorphous carbon (a-C) films offer a prime solution to optimize surface roughness due to their outstanding characteristics. In this study, hydrogenated a-C films are deposited on two-dimensional quartz plates and three-dimensional quartz molds to evaluate the growth mechanisms and changes in the surface roughness, which is supported by molecular dynamics simulations. Results reveal that surface roughness encounters multiple variations until it reaches stable values. These fluctuations are categorized into four different stages which provide a concrete understanding of various growing mechanisms at each stage. Different behavior of the atoms in the top layers is recorded in the cases of normal and grazing incidents of carbon atoms. Lower surface roughness values are obtained at low-angle deposition. Interestingly, surface smoothing is attained on the sidewalls of the nanotrench mold where the deposition occurs with high incident ion angles.
Nonlocality of mixtures of the ground and first excited states within

J

1

−

J

2

Heisenberg model
Authors: Bao Jia , Shen Longhui , Liu Hongying , Guo Bin , Sun Zhaoyu 
Published: July 2024

Abstract
We investigate both bipartite and multipartite nonlocality in the

J

1

−

J

2

Heisenberg model. Bipartite nonlocality is measured by the Clauser–Horne–Shimony–Holt inequality, while multipartite nonlocality is explored through Bell-type inequalities. Our findings reveal that neither ground-state nor full thermal-state nonlocality reliably characterizes quantum phase transitions (QPTs). However, we uncover that the mixed-state nonlocality of the ground and first excited states exhibits distinctive characteristics applicable to both bipartite and multipartite scenarios. We also demonstrate how mixed-state quantum correlation behaviors depend on varying temperature regimes. In the bipartite case, we observe a phenomenon known as ‘correlation reversal’ with increasing temperature, a previously unreported occurrence in other models. For the multipartite case, the ability to signify phase transitions is significantly enhanced as the temperature rises. Furthermore, we discover a linear scaling effect that provides valuable insights for extrapolating QPTs in the thermodynamic limit as

N
→
∞

. Additionally, we identify the critical temperature at which mixed-state nonlocality becomes a reliable indicator of phase transitions.
Application of wavelet dynamic joint adaptive network guided by pseudo-label alignment mechanism in gearbox fault diagnosis
Authors: Shao Zhenfa , Jiang Hong , Zhang Xiangfeng , Zhou Jianyu , Hu Xiaoheng 
Published: July 2024

Abstract
In practical scenarios, gearbox fault diagnosis faces the challenge of extremely scarce labeled data. Additionally, variations in operating conditions and differences in sensor installations exacerbate data distribution shifts, significantly increasing the difficulty of fault diagnosis. To address the above issues, this paper proposes a wavelet dynamic joint self-adaptive network guided by a pseudo-label alignment mechanism (MDJSN-DFL). First, the wavelet-efficient convolution module is designed based on wavelet convolution and efficient attention mechanisms. This module is used to construct a multi-wavelet convolution feature extractor to extract critical fault features at multiple levels. Secondly, to improve the classifier’s discriminability in the target domain, a transitional clustering-guided DFL is developed. This mechanism can capture fuzzy classification samples and improve the pseudo-label quality of the target domain. Finally, a dynamic joint mean square difference algorithm (DJSD) is proposed, which is composed of joint maximum mean square discrepancy and joint maximum mean discrepancy. The algorithm can adaptively adjust according to the dynamic balance factor to minimize the domain distribution discrepancy. Experiments on two different gearbox datasets show that MDJSN-DFL performs better in diagnostic scenarios under varying load conditions and different sensor installation setups, validating the proposed method’s effectiveness and superiority.
Existence and stability of weak solutions of the Vlasov–Poisson system in localised Yudovich spaces
Authors: Crippa Gianluca , Inversi Marco , Saffirio Chiara , Stefani Giorgio 
Published: July 2024

Abstract
We consider the Vlasov–Poisson system both in the repulsive (electrostatic potential) and in the attractive (gravitational potential) cases. Our first main theorem yields the analog for the Vlasov–Poisson system of Yudovich’s celebrated well-posedness theorem for the Euler equations: we prove the uniqueness and the quantitative stability of Lagrangian solutions

f
=
f
(
t
,
x
,
v
)

whose associated spatial density

ρ
f

=

ρ
f

(
t
,
x
)

is potentially unbounded but belongs to suitable uniformly-localised Yudovich spaces. This requirement imposes a condition of slow growth on the function

p
↦
‖

ρ
f

(
t
,
⋅
)

‖

L
p

uniformly in time. Previous works by Loeper, Miot and Holding–Miot have addressed the cases of bounded spatial density, i.e.

‖

ρ
f

(
t
,
⋅
)

‖

L
p

≲
1

, and spatial density such that

‖

ρ
f

(
t
,
⋅
)

‖

L
p

∼

p

1

/

α

for

α
∈
[
1
,
+
∞
)

. Our approach is Lagrangian and relies on an explicit estimate of the modulus of continuity of the electric field and on a second-order Osgood lemma. It also allows for iterated-logarithmic perturbations of the linear growth condition. In our second main theorem, we complement the aforementioned result by constructing solutions whose spatial density sharply satisfies such iterated-logarithmic growth. Our approach relies on real-variable techniques and extends the strategy developed for the Euler equations by the first and fourth-named authors. It also allows for the treatment of more general equations that share the same structure as the Vlasov–Poisson system. Notably, the uniqueness result and the stability estimates hold for both the classical and the relativistic Vlasov–Poisson systems.
Phytochemical preparation of Zinc Stannate nanoparticles by using lemon and grapefruit peels for removal of cadmium ions
Authors: Salova Aneta , Mohealdeen Sura Mohammad , Hussein Abbas Hameed Abdul , Hasan Dheyaa Flayih , Mushtaq Hiba , Idan Ameer Hassan , Fallah Amer Ramadan 
Published: July 2024

Abstract
The synthesis of Zinc Stannate (Zn2SnO4) nanoparticles may be achieved by utilizing lemon and grapefruit peels, as indicated by the results of this study. This analysis outlines a sustainable, cost-effective, and readily available approach. The Zn2SnO4 nanoparticles were generated biologically and were discovered to have a cubic crystalline structure, as established by structural analysis using Rietveld refinement. TEM microstructural examinations revealed that Zn2SnO4 nanoparticles exhibit a homogeneous distribution and possess an average diameter of around 21 nm. The Zn2SnO4 nanoparticles have an optical energy band gap of 3.05 eV and demonstrate a UV region peak, which showed that Zn2SnO4 nanoparticles were being formed. More precisely, the pH of the solution greatly affects the absorption of Cd2+ ions. Kinetic analysis involves the use of two types of models: pseudo-first-order and pseudo-second-order. The previous model yields an R2 value of 0.9031 and a rate constant (k1) of 0.41 × 10−2 min−1. However, the pseudo-second-order model provides a better match, as seen by its very high R2 value of 0.9932 and rate constant (k2) value of 4.4 × 10−3 g.(mg.min)−1. In addition, isotherm modeling shows that the experimental data closely match the Freundlich isotherm model.
Automated shot-to-shot optimization of the plasma start-up scenario in the TCV tokamak
Authors: di Grazia Luigi Emanuel , Felici Federico , Mattei Massimiliano , Merle Antoine , Molina Pedro , Galperti Cristian , Coda Stefano , Duval Basil , Maier Antoine , Mele Adriano , Perek Artur , Pironti Alfredo , Ravensbergen Timo , Vincent Benjamin , Wüthrich Curdin ,   the TCV Team
Published: July 2024

Abstract
Plasma start-up is typically achieved manipulating poloidal magnetic fields, gas injection and possibly auxiliary heating. Model-based design techniques have been gaining increasing attention in view of future large tokamaks which have more stringent constraints and less room for trial-and-error. In this paper, we formulate the tokamak start-up scenario design problem as a constrained optimization problem and introduce a novel shot-to-shot correction algorithm, based on the Iterative Learning Control concept, to compensate for unavoidable modeling errors based on experimental data. The effectiveness of the approach is demonstrated in experiments on the TCV tokamak showing that the target ramp-up scenario could be obtained in a small number of shots with a rough electromagnetic model.
Transfer learning may explain pigeons’ ability to detect cancer in histopathology
Authors: Kilim Oz , Báskay János , Biricz András , Bedőházi Zsolt , Pollner Péter , Csabai István 
Published: July 2024

Abstract
Pigeons’ unexpected competence in learning to categorize unseen histopathological images has remained an unexplained discovery for almost a decade (Levenson et al 2015 PLoS One
10 e0141357). Could it be that knowledge transferred from their bird’s-eye views of the earth’s surface gleaned during flight contributes to this ability? Employing a simulation-based verification strategy, we recapitulate this biological phenomenon with a machine-learning analog. We model pigeons’ visual experience during flight with the self-supervised pre-training of a deep neural network on BirdsEyeViewNet; our large-scale aerial imagery dataset. As an analog of the differential food reinforcement performed in Levenson et al’s study 2015 PLoS One
10 e0141357), we apply transfer learning from this pre-trained model to the same Hematoxylin and Eosin (H&E) histopathology and radiology images and tasks that the pigeons were trained and tested on. The study demonstrates that pre-training neural networks with bird’s-eye view data results in close agreement with pigeons’ performance. These results support transfer learning as a reasonable computational model of pigeon representation learning. This is further validated with six large-scale downstream classification tasks using H&E stained whole slide image datasets representing diverse cancer types.
Effects of extreme stratospheric polar vortex events on near-surface wind gusts across Europe
Authors: Utrabo-Carazo Eduardo , Lockwood Julia F , Dunn Robert J H , Minola Lorenzo , Aguilar Enric , Azorin-Molina Cesar 
Published: July 2024

Abstract
Extreme stratospheric polar vortex (SPV) events can influence winter tropospheric circulation for up to 60 d. Their impacts on air temperature have been extensively studied recently. However, there is a research gap in their effects on wind speeds and its extremes. This study aims to evaluate, for the first time, the impacts of such extreme SPV events on observed and modelled near-surface wind gusts across Europe. We have analysed wind gust data coming from: station-based observations (for the Iberian Peninsula and Scandinavia), the ERA5 reanalysis and the SEAS5 and GloSea6 seasonal forecasting systems. We assess their similarities in reproducing 4 parameters of their corresponding distributions: median, standard deviation, skewness and kurtosis. For all these datasets, the results indicate that extreme positive SPV events are followed by negative wind gust anomalies in Southern Europe and positive in Northern Europe. Whereas, negative SPV events (such as Sudden Stratospheric Warmings) have positive gust anomalies in Southern Europe and negative in the north. A central region shows negligible anomalies in both cases. This highlights the ability of SPV as a predictor for short-medium-term forecasting of extreme wind events, which would have direct applications to many socioeconomic and environmental issues such as the estimation of wind-power generation.
Evaluation of tetrachloroethylene (PCE) and its degradation products in human exhaled breath and indoor air in a community setting
Authors: Lee Jung Hyun , Bryant Alaina K , Alajlouni Marwan , Boor Brandon E , Tasoglou Antonios , Liu Sa 
Published: July 2024

Abstract
Tetrachloroethylene (PCE) is a widely utilized volatile chemical in industrial applications, including dry cleaning and metal degreasing. Exposure to PCE potentially presents a significant health risk to workers as well as communities near contamination sites. Adverse health effects arise not only from PCE, but also from PCE degradation products, such as trichloroethylene (TCE) and vinyl chloride (VC). PCE, TCE, and VC can contaminate water, soil, and air, leading to exposure through multiple pathways, including inhalation, ingestion, and dermal contact. This study focused on a community setting in Martinsville, Indiana, a working-class Midwestern community in the United States, where extensive PCE contamination has occurred due to multiple contamination sites (referring to ‘plumes’), including a Superfund site. Utilizing proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS), PCE, TCE, and VC concentrations were measured in the exhaled breath of 73 residents from both within and outside the plume areas. PCE was detected in 66 samples, TCE in 26 samples, and VC in 68 samples. Our results revealed a significant positive correlation between the concentrations of these compounds in exhaled breath and indoor air (Pearson correlation coefficients: PCE = 0.75, TCE = 0.71, and VC = 0.89). This study confirms the presence of PCE and its degradation products in exhaled breath in a community exposure investigation, demonstrating the potential of using exhaled breath analysis in monitoring exposure to environmental contaminants. This study showed the feasibility of utilizing PTR-TOF-MS in community investigations to assess exposure to PCE and its degradation products by measuring these compounds in exhaled breath and indoor air.
Synthesis of NO by rotating sliding arc discharge reactor with conical-spiral electrodes
Authors: CHEN 陈 Bingyan 秉岩 , LIU 刘 Qi 琦 , LI 李 Xu 旭 , ZHANG 张 Chunyang 春阳 , GUO 郭 Xiangbin 湘彬 , YU 余 Qingzhou 青洲 , TANG 唐 Zhicheng 志承 , HE 何 Xiang 湘 , SU 苏 Wei 巍 , JIANG 蒋 Yongfeng 永锋 
Published: July 2024

Abstract
The present work investigates the potential applications of nitrogen oxides (NO
x
), particularly nitric oxide (NO) and nitrogen dioxide (NO2), generated through discharge plasma in diverse sectors such as medicine, nitrogen fixation, energy, and environmental protection. In this study, a rotating sliding arc discharge reactor was initially employed to produce high concentrations of gaseous NO
x
, followed by the utilization of a molybdenum wire redox reactor for NO2-to-NO conversion. The outcomes reveal that the discharge states and generations of NO
x
are affected by varying parameters, including the applied energies, frequencies and airflow states (1.3–2.6 m/s are the laminar flow, 2.6–5.2 m/s are the transition state, 5.2–6.5 m/s are the turbulent flow), and the concentrations of NO
x
within the arc discharge are higher than that in the spark discharge. Moreover, the concentrations of NO, NO2 and NO
x
gradually increased, and the concentration ratios of NO/NO2 and NO
x
/NO2 decreased with increasing the applied energy for one cycle from 14.8 mJ to 24.3 mJ. Meanwhile, the concentrations of NO, NO2 and NO
x
gradually decreased, and the concentration ratios of NO/NO2 and NO
x
/NO2 first decreased and then increased with increasing the applied frequencies from 5.0 kHz to 9.0 kHz. Further, the concentrations of NO, NO2 and NO
x
gradually decreased, and the concentration ratios of NO/NO2 and NO
x
/NO2 first increased and then decreased with increasing the air flow speeds from 1.3 m/s to 6.5 m/s. Lastly, the concentrations of NO increased and NO2 decreased with increasing temperature from 25 °C to 400 °C using molybdenum converted. These findings provide experimental support for the application of plasma in the fields of medicine, nitrogen fixation, energy and environmental protection.
Predictive models for inorganic materials thermoelectric properties with machine learning
Authors: Don-tsa Delchere , Mohou Messanh Agbeko , Amouzouvi Kossi , Maaza Malik , Beltako Katawoura 
Published: July 2024

AbstractThe high computational demand of the Density Functional Theory (DFT) based method for screening new materials properties remains a strong limitation to the development of clean and renewable energy technologies essential to transition to a carbon-neutral environment in the coming decades. Machine Learning comes into play with its innate capacity to handle huge amounts of data and high-dimensional statistical analysis. In this paper, supervised Machine Learning models together with data analysis on existing datasets obtained from a high-throughput calculation using Density Functional Theory are used to predict the Seebeck coefficient, electrical conductivity, and power factor of inorganic compounds. The analysis revealed a strong dependence of the thermoelectric properties on the effective masses, we also proposed a machine learning model for the prediction of highly performing thermoelectric materials which reached an efficiency of 95 percent. The analyzed data and developed model can significantly contribute to innovation by providing a faster and more accurate prediction of thermoelectric properties, thereby, facilitating the discovery of highly efficient thermoelectric materials.
On the benefit of attention in inverse design of thin films filters
Authors: Hadad Barak , Oren Omry , Bahabad Alon 
Published: July 2024

Abstract
Attention layers are a crucial component in many modern deep learning models, particularly those used in natural language processing and computer vision. Attention layers have been shown to improve the accuracy and effectiveness of various tasks, such as machine translation, image captioning, etc. Here, the benefit of attention layers in designing optical filters based on a stack of thin film materials is investigated. The superiority of Attention layers over fully-connected Deep Neural Networks is demonstrated for this task.
Risks of source and species-specific air pollution for COVID-19 incidence and mortality in Los Angeles
Authors: Yang Lin-Syuan , Kleeman Michael J , Cushing Lara J , Lipsitt Jonah , Su Jason , Burnett Richard T , Batteate Christina M , Nau Claudia L , Young Deborah R , Tartof Sara Y , Butler Rebecca K , Padilla Ariadna , jerrett Michael 
Published: July 2024

Abstract
Growing evidence from ecological studies suggests that chronic exposure to standard air pollutants (PM2.5, NO2, and ozone) exacerbates risks of coronavirus 2 (COVID-19) incidence and mortality. This study assessed the associations between an expanded list of air pollutants and COVID-19 incidence and mortality in Los Angeles. Annual mean exposure to air pollutants in 2019—including PM0.1 mass, PM2.5 mass, PM2.5 elemental carbon (EC), PM2.5 tracer from mobile sources, NO2, and ozone—were estimated at the ZIP code level in residential areas throughout Los Angeles. Negative binomial models and a spatial model were used to explore associations between health outcomes and exposures in single pollutant and multi-pollutant models. Exposure to PM0.1 mass, ozone, NO2, and PM2.5 EC were identified as risk factors for COVID-19 incidence and mortality. The results also suggest that PM2.5 and NO2 together may have synergistic effects on harmful COVID-19 outcomes. The study provides localized insights into the spatial and temporal associations between species-specific air pollutants and COVID-19 outcomes, highlighting the potential for policy recommendations to mitigate specific aspects of air pollution to protect public health.
Assessing the impact of afforestation as a natural climate solution in the Canadian boreal
Authors: du Toit Francois , Coops Nicholas C , Mulverhill Christopher , Toomey Aoife 
Published: July 2024

Abstract
Natural climate solutions (NCSs) are conservation, restoration, and improved land management actions that have potential to provide climate mitigation across different land cover types. NCS related to forests offer a significant portion of cost-effective NCS mitigation required to limit warming to below 2 °C. Afforestation—planting trees in areas where forests can occur but does not currently exist has been proposed as a viable NCS. Here, we examine how long-term, medium resolution satellite datasets and physiological growth models can be used to inform potential carbon accumulation from forest afforestation. We leverage free and open Landsat-derived datasets to examine potential increases in aboveground biomass (AGB) and tons of CO2 equivalent (CO2e) that afforestation may provide by 2050 in the Canadian boreal. We utilized contemporary Landsat-scale definitions of land cover, forest age, and species datasets to identify opportunities for new forest growth in areas previously unforested across study sites. These datasets, along with terrain and climate, were used as inputs for the 3-PG physiological growth model, which converts solar radiation into net primary productivity on a monthly time-step, and was parameterized for key natural species to simulate forest growth and carbon accumulation under three different future climate scenarios. We compared these new fine-scale, climate-adapted estimates with previous findings. The amount of CO2e sequestered per hectare per year increased with increasing CO2 emissions (4.0%–12.4% more carbon). Using a reference area, the fertilized simulation sequestered 24.38 Tg CO2e yr−1 in 2050 compared to 24.9 Tg CO2e yr−1 proposed in other research under the warmest scenario. The use of physiological models linked to satellite data to support NCS calculations, particularly for unforested areas, is a new application. The results highlight the potential for 3-PG to be used to estimate AGB and provide valuable information for the performance of NCS under a changing climate.
Shcherbytsky’s Leadership between Moscow and the Regions
Authors: Kibita Nataliya 
Published: July 2024

Abstract
Shelest’s successor Volodymyr Shcherbytsky played by the rules. Shcherbytsky projected an image of a strong leader. But the Ukrainian leadership remained weak. The limits of the Ukrainian leadership’s authority became visible with the Chornobyl nuclear catastrophe. Gorbachev’s perestroika undermined what little authority the Ukrainian leadership had by the mid-1980s. But Gorbachev’s decision to correct deformations in the nationality policy gave Shcherbytsky hope that the central leadership would address the imbalance between the responsibilities and powers of the republics, increase their autonomy, and formalize the relationship between the centre and the republics. Throughout Shcherbytsky’s tenure, the Communist Party of Ukraine (CPU) remained fragmented. Obkom secretaries continued to rely on local managers for the socio-economic agenda and fulfilling agricultural plans. Party–industry alliances remained in place. Perestroika revealed both the fragmentation of the CPU and the distinctiveness of Western Ukraine. Gorbachev’s call to increase the self-governing of Soviet society and glasnost prompted obkom secretaries to urge Shcherbytsky to concentrate on their needs. Under pressure from the regions, on 31 December 1988, Shcherbytsky wrote a letter to Gorbachev in which he defended the institutional interests of the Ukrainian leadership. Pending a reaction from Moscow, under the pressure of a deteriorating economic situation and rapid liberalization of political life, obkom secretaries continued to insist that Shcherbytsky deviate from defending the interests of all-union agencies, focus on the socio-economic needs of the Ukrainian regions, and empower the regions. Shcherbytsky refused to formally empower the obkoms and oblispolkoms and, at the end of September 1989, left the post.