bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning-based prediction of operation conditions from plasma plume images of atmospheric-pressure plasma reactors
Authors: Bong Cheolwoo , Kim Byeong Soo , Ali Mohammed H A , Kim Dongju , Bak Moon Soo 
Published: April 2023

Abstract
A technique was proposed in this paper to monitor the key operating conditions of a plasma abatement system, which are the concentration of the carbon-containing process gas and the treatment flowrate, from a plasma plume image acquired using an inexpensive color camera. The technique is based on the observation that the shape and color of the plasma plume vary with the variations in the specific energy input and plasma gas composition. In addition, because these variations are marginal and it is challenging to identify an analytical relationship between these variations and the operating conditions, the prediction model is obtained in a data-driven manner. Specifically, the model was composed of a set of convolutional autoencoders (CAEs) and a dense neural network. Furthermore, it was trained only with images captured under normal operation so that (1) images captured under abnormal operations could be identified based on the reconstruction error of the trained CAEs and (2) predictions are made only on normal images. As a demonstration, methane was tested as a process gas, and oxygen was used as a reaction agent in a nitrogen-rich environment. The test results showed that the optimized model could predict the treatment flowrate and process gas concentration with 96% probability within ±3.08 slpm and ±300 ppm, respectively.
An imbalanced sample intelligent fault diagnosis method using data enhancement and improved broad learning system
Authors: Lu Jiantao , Cui Rongqing , Li Shunming 
Published: April 2023

Abstract
Broad learning system (BLS) has been widely applied in the field of fault diagnosis because of its high computational efficiency, simple structure, and strong interpretability. However, traditional BLS cannot extract deep level fault features. Meanwhile, some fault samples are difficult to obtain, which leads to the imbalance of samples and further affects the diagnostic results of BLS. To solve these problems, an improved BLS fault diagnosis method based on data enhancement and multi-domain feature fusion is proposed. First, to solve the problem of sample imbalance, some false samples are generated through deep convolutional generative adversarial networks. Second, to solve the problem of poor feature extraction ability of BLS, the multi-domain feature extraction and feature optimization based on ReliefF algorithm are carried out for the enhanced samples. Compared with traditional BLS, the improved BLS effectively solves the problem of sample imbalance and greatly improves the diagnostic accuracy. The proposed method is then testified on the rolling bearing fault simulation test bench. The results show that, samples generated by the proposed method are highly similar to the real samples. In addition, the diagnostic accuracy of the BLS after multi-domain feature extraction and optimization is improved by about 19.67%, which proves the effectiveness of the method. This method provides a new perspective in fault diagnosis and could further expand the application of BLS in fault diagnosis.
Recursive spatial-temporal clustering-based target detection with millimeter-wave radar point cloud
Authors: Bi Zhicheng , Gao Yu , Wang Chaofeng , Liu Zhenghai , Wan Yaping , Yang Xiaohua 
Published: April 2023

Abstract
Millimeter-wave (mmWave) radar plays a vital role in a wide range of applications such as security surveillance and environmental monitoring. This work investigates target detection with radar point cloud measurements in the slow-motion scenario. In contrast to the existing spatial domain clustering-based target detection methods, we adopt a recursive spatial-temporal clustering (STC)-based method to detect targets in the spatial and temporal domain jointly. Specifically, the points belonging to targets are obtained by clustering with a distance metric defined in the spatial-temporal domains. In addition, to ensure the feasibility of the proposed method for practical real-time implementation, a speed-up scheme that intends to reduce the computational complexity induced by clustering in both spatial and temporal dimensions is developed. We demonstrate the efficacy of the proposed recursive STC-based method through experimental mmWave radar point cloud data where multiple people walk simultaneously in an open space. The proposed method achieves decent target detection performance improvement compared to a widely-used clustering method for target detection while its computation time is negligible compared to radar data reception time.
Preparation of carnation-like Ag-ZnO composites for enhanced photocatalysis under visible light
Authors: Vu Anh-Tuan , Mac Van Hung , Nguyen Thanh Hung , Nguyen Thu Huong 
Published: April 2023

Abstract
Carnation-like ZnO was synthesized by the facile precipitation method (at room temperature and in 120 min) to decompose dyes in an aqueous medium. The carnation-like ZnO had a stratified porous structure with a size of about 2–3 μm, its petals had a smooth surface with a thickness of 5–10 nm and a width of about 300–500 nm. Ag-ZnO composites were synthesized using glucose with the assistance of PVP. The morphology of Ag-ZnO composites was almost unchanged compared to ZnO. Where, the Ag nanoparticles in the size range of 5–15 nm were uniformly dispersed on the ZnO petals, improving the catalytic ability of the composites in tartrazine (TA) degradation. The influence of Ag content on catalytic structure and performance of composite was studied. The 5Ag-ZnO sample had the highest BET surface area and pore volume and the lowest gap energy (Eg) among the as-synthesized samples. The 5Ag-ZnO sample proclaimed the degradation efficiency in 70 min of 97.8% and the k
ap of 0.031 min−1. The influences of catalyst content, solution pH, and concentration of dye on the photodegradation efficiency of the composite were thoroughly studied. Besides, the photocatalytic activity of the composite was demonstrated by degrading various organic substances and reusability. In addition, it was compared to a metal-semiconductor catalyst of Au-ZnO and semiconductor-semiconductor catalysts of MoS2-ZnO, Cu2O-ZnO, and SiO2-ZnO. The catalytic mechanism under visible light was proposed.
Porphyrin conjugated polymer/Pt-loaded graphite carbon nitride nanocomposites for efficient charge separation and broadband responsive H2 evolution
Authors: Zeng Peng , Xu Lei , Jia Huaiyang , Liang Cuien , Li Renjie , Peng Tianyou 
Published: April 2023

Abstract
An efficient broadband responsive two-dimensional (2D) heterometallic Zn-/Co-porphyrin conjugated polymer (ZnCoP-F CP) with its Co-porphyrin bridging unit bearing two perfluorophenyls is coupled with 2.0 wt% Pt-loaded graphite carbon nitride (PCN) to fabricate a novel 2D/2D nanocomposite (ZnCoP-F/PCN). The resultant ZnCoP-F/PCN composite with an optimal mass ratio exhibits broadband (UV–vis–NIR) responsive H2 evolution reaction (HER) activity up to 432 μmol h−1, 5.2 and 2.8 times higher than that of the ZnCoP-F CP (83 μmol h−1) and PCN (151 μmol h−1) alone, respectively. Furthermore, the ZnCoP-F/PCN displays excellent apparent quantum yields (AQY) of 18.2%, 18.3%, 17.6%, 16.5%, 13.9%, 8.7%, 5.1%, 4.3%, 1.9%, 0.95% and 0.62% at 350, 380, 420, 450, 500, 550, 600, 700, 785, 850 and 950 nm, which are also higher than that of ZnCoP-F CP illuminated at the respective monochromatic light. The enhanced broadband responsive HER performance of ZnCoP-F/PCN can be attributed to the easily assembled ZnCoP-F CP and PCN nanosheets through strong π–π stacking interaction, which can facilitate the fast charge transfer from ZnCoP-F CP to PCN for HER. This work opens a new pathway to fabricate porphyrin polymer-based nanocomposite for more efficiently converting solar radiation and water into H2.
The strong vanishing viscosity limit with Dirichlet boundary conditions
Authors: Kelliher James P 
Published: April 2023

Abstract
We adapt methodology of Tosio Kato to establish necessary and sufficient conditions for the solutions to the Navier–Stokes equations with Dirichlet boundary conditions to converge in a strong sense to a solution to the Euler equations in the presence of a boundary as the viscosity is taken to zero. We extend existing conditions for no-slip boundary conditions to allow for nonhomogeneous Dirichlet boundary conditions and curved boundaries, establishing several new conditions as well. We give a brief comparison of various correctors appearing in the literature used for similar purposes.
Global martingale and pathwise solutions and infinite regularity of invariant measures for a stochastic modified Swift–Hohenberg equation
Authors: Wang Jintao , Zhang Xiaoqian , Li Chunqiu 
Published: April 2023

Abstract
We consider a 2D stochastic modified Swift–Hohenberg equations with multiplicative noise and periodic boundary. First, we establish the existence of local and global martingale and pathwise solutions in the regular Sobolev space

H

2
m

for each

m
⩾
1

. Associated with the unique global pathwise solution, we obtain a Markovian transition semigroup. Then, we show the existence of invariant measures and ergodic invariant measures for this Markovian semigroup on

H

2
m

. At last, we improve the regularity of the obtained invariant measures to

H

2
(
m
+
1
)

. With appropriate conditions on the diffusion coefficient, we can deduce the infinite regularity of the invariant measures, which was conjectured by Glatt-Holtz et al in their situation (2014 J. Math. Phys.
55 277–304).
A pedagogical experimental setup to measure quantum transport
Authors: Caiza Byron , Medina Ernesto , Sabater Carlos , Brämer-Escamilla Werner 
Published: April 2023

Abstract
We designed and built a compact device capable of measuring electronic transport in atomic-sized contacts. This experimental setup is based on the break-junction (BJ) approach. According to the samples used as electrodes, we named the instrument a Surface-Tip Break Junction. Our instrument, like a scanning tunnelling microscope in the BJ configuration, can measure the quantum transport in atoms captured by electrodes. In our case, to measure this transport, we included a homemade I–V converter, a low-cost oscilloscope, and a mobile phone that functions as a data acquisition system. Moreover, we developed a protocol to analyse the digitized data obtained from the oscilloscope. This low-cost pedagogical setup offers the possibility of implementation in undergraduate and graduate laboratories.
Investigating spatial variations in dynamic cerebral autoregulation through a computational model of stenosis
Authors: Tong Zheng , Payne Stephen J 
Published: April 2023

Abstract

Objective. Dynamic cerebral autoregulation (dCA) is a well-established mechanism that acts to maintain cerebral blood flow (CBF) reasonably constant in response to short-term fluctuations in blood pressure. It is known to be impaired in many clinical conditions, including stenosis, which is also a major risk factor for ischaemic stroke. However, it is not yet well understood whether impairment in dCA in one brain region is independent or not on dCA impairment in other brain regions, for example, whether there are spatial effects of stenosis on dCA. This is due to the complex blood flow environment and the lack of physiological experiments. Approach. We thus establish and apply a novel computational stenosis model including the circle of Willis to investigate and to quantify the degree of dCA impairment and CBF patterns as a function of stenosis fraction, measured in different configurations of the cerebral vasculature. Main results. We find some evidence for dependence between dCA in different brain regions, although this is very preliminary and much more experimental data will be required to answer this question fully. Significance. Our study has provided a first attempt to consider the effect of stenosis in various arteries on cerebral autoregulation to investigate spatial variations in dCA. This has potential applications in the treatment of cerebrovascular diseases where the control of cerebral perfusion is critical but where measurements are scarce.
Melting, reentrant ordering and peak effect for Wigner crystals with quenched and thermal disorder
Authors: Reichhardt C , Reichhardt C J O 
Published: April 2023

Abstract
We consider simulations of Wigner crystals in solid state systems interacting with random quenched disorder in the presence of thermal fluctuations. When quenched disorder is absent, there is a well defined melting temperature determined by the proliferation of topological defects, while for zero temperature, there is a critical quenched disorder strength above which topological defects proliferate. When both thermal and quenched disorder are present, these effects compete, and the thermal fluctuations can reduce the effectiveness of the quenched disorder, leading to a reentrant ordered phase in agreement with the predictions of Nelson (1983 Phys. Rev. B 27 2902–14). There are two competing theories for the low temperature behavior, and our simulations show that both capture aspects of the actual response. The critical disorder strength separating ordered from disordered states remains finite as the temperature goes to zero, as predicted by Cha and Fertig (1995 Phys. Rev. Lett.
74 4867–70), instead of dropping to zero as predicted by Nelson. At the same time, the critical disorder strength decreases with decreasing temperature, as predicted by Nelson, instead of remaining constant, as predicted by Cha and Fertig. The onset of the reentrant phase can be deduced based on changes in the transport response, where the reentrant ordering appears as an increase in the mobility or the occurrence of a depinning transition. We also find that when the system is in the ordered state and thermally melts, there is an increase in the effective damping or pinning. This produces a drop in the electron mobility that is similar to the peak effect phenomenon found in superconducting vortices, where thermal effects soften the lattice or break down its elasticity, allowing the particles to better adjust their positions to take full advantage of the quenched disorder.
Grey-level intensity measurements processing by means of Volterra equations and Least Squares Method for Video restoration
Authors: Cuesta E , Finat J , Sánchez J 
Published: April 2023

Abstract
In this paper a video sequence is considered as a two dimensional time evolving process. Under this assumption two Volterra equations based mathematical models are introduced for video restoration purposes. The first one is based on geometric features related to the spatial–time structure of the video sequence and gives rise to a nonlinear Volterra equation. This model arises from the Mean Curvature Flow linked to evolving surfaces. The second one is based on analytic features and leads to the formulation of a linear Volterra model. The second procedure relies on the assumption of local time coherence in the sequence of frames, at least short back in time. In both cases Volterra equations based approach introduces a memory effect in the process which in the present terminology means that several frames back may be taken into account for the better reconstruction of the current frame throughout a convenient choice of the convolution kernel. On the other hand the role played by Least Squares Method focuses on the practical computation of that convolution kernel just at discrete level. The performance of both approaches is shown through a list of suitable experiments, and the better performance of the second approach is illustrated with remarkable improvements in critical cases.
Climate-driven land surface phenology advance is overestimated due to ignoring land cover changes
Authors: Pan Yuhao , Peng Dailiang , Chen Jing M , Myneni Ranga B , Zhang Xiaoyang , Huete Alfredo R , Fu Yongshuo H , Zheng Shijun , Yan Kai , Yu Le , Zhu Peng , Shen Miaogen , Ju Weimin , Zhu Wenquan , Xie Qiaoyun , Huang Wenjiang , Chen Zhengchao , Huang Jingfeng , Wu Chaoyang 
Published: April 2023

Abstract
Global warming has led to earlier spring green-up dates (GUDs) in recent decades with significant consequences for global carbon and hydrologic cycles. In addition to changes in climate, land cover change (LCC), including interchanges between vegetation and non-vegetation, and among plants with different functional traits, may also affect GUD. Here, we analyzed how satellite-derived GUD from 1992 to 2020 was impacted by changes in temperature, precipitation, standardized precipitation evapotranspiration index (SPEI), solar radiation, and LCC for the Northern Hemisphere (>30° N). While the climate variables had larger impact overall, variability in GUD was controlled by LCC for 6% of the Northern Hemisphere, with systematically earlier or later changes among transitions between different land cover types. These changes were found mainly along the southeastern coast of the United States, in Central-north Europe, and across northeastern China. We further showed that climate change attribution of earlier GUD during 1992–2020 was overestimated by three days when the impact of LCC was ignored. Our results deepen the understanding of how LCC impacts GUD variability and enables scientists to more accurately evaluate the impact of climate change on land surface phenology.
Floquet engineering of magnetism in topological insulator thin films
Authors: Liu Xiaoyu , Fan Benshu , Hübener Hannes , De Giovannini Umberto , Duan Wenhui , Rubio Angel , Tang Peizhe 
Published: April 2023

Abstract
Dynamic manipulation of magnetism in topological materials is demonstrated here via a Floquet engineering approach using circularly polarized light. Increasing the strength of the laser field, besides the expected topological phase transition (PT), the magnetically doped topological insulator thin film also undergoes a magnetic PT from ferromagnetism to paramagnetism, whose critical behavior strongly depends on the quantum quenching. In sharp contrast to the equilibrium case, the non-equilibrium Curie temperatures vary for different time scale and experimental setup, not all relying on change of topology. Our discoveries deepen the understanding of the relationship between topology and magnetism in the non-equilibrium regime and extend optoelectronic device applications to topological materials.
Randomised controlled trials of antihypertensive therapy: does exclusion of orthostatic hypotension alter treatment effect? A systematic review and meta-analysis
Authors: Reddin Catriona , Murphy Robert , Hanrahan Caoimhe , Loughlin Elaine , Ferguson John , Judge Conor , Waters Ruairi , Canavan Michelle , Kenny Rose Anne , O’Donnell Martin 
Published: April 2023

Abstract

Background and purpose
Management of antihypertensive therapy is challenging in patients with symptomatic orthostatic hypotension, a population often excluded from randomised controlled trials of antihypertensive therapy. In this systematic review and meta-analysis, we sought to determine whether the association of antihypertensive therapy and adverse events (e.g. falls, syncope), differed among trials that included or excluded patients with orthostatic hypotension.

Methods
We performed a systematic review and meta-analysis of randomised controlled trials comparing blood pressure lowering medications to placebo, or different blood pressure targets on falls or syncope outcomes and cardiovascular events. A random-effects meta-analysis was used to estimate a pooled treatment-effect overall in subgroups of trials that excluded patients with orthostatic hypotension and trials that did not exclude patients with orthostatic hypotension, and tested P for interaction. The primary outcome was fall events.

Results
46 trials were included, of which 18 trials excluded orthostatic hypotension and 28 trials did not. The incidence of hypotension was significantly lower in trials that excluded participants with orthostatic hypotension (1.3% versus 6.2%, P &lt; 0.001) but not incidences of falls (4.8% versus 8.8%; P = 0.40) or syncope (1.5% versus 1.8%; P = 0.67). Antihypertensive therapy was not associated with an increased risk of falls in trials that excluded (OR 1.00, 95% CI; 0.89–1.13) or included (OR 1.02, 95% CI; 0.88–1.18) participants with orthostatic hypotension (P for interaction = 0.90).

Conclusions
The exclusion of patients with orthostatic hypotension does not appear to affect the relative risk estimates for falls and syncope in antihypertensive trials.
Predicting future falls in older people using natural language processing of general practitioners’ clinical notes
Authors: Dormosh Noman , Schut Martijn C , Heymans Martijn W , Maarsingh Otto , Bouman Jonathan , van der Velde Nathalie , Abu-Hanna Ameen 
Published: April 2023

AbstractBackgroundFalls in older people are common and morbid. Prediction models can help identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity to develop automated prediction tools that may help to identify fall-prone individuals and lower clinical workload. However, existing models primarily utilise structured EHR data and neglect information in unstructured data. Using machine learning and natural language processing (NLP), we aimed to examine the predictive performance provided by unstructured clinical notes, and their incremental performance over structured data to predict falls.MethodsWe used primary care EHR data of people aged 65 or over. We developed three logistic regression models using the least absolute shrinkage and selection operator: one using structured clinical variables (Baseline), one with topics extracted from unstructured clinical notes (Topic-based) and one by adding clinical variables to the extracted topics (Combi). Model performance was assessed in terms of discrimination using the area under the receiver operating characteristic curve (AUC), and calibration by calibration plots. We used 10-fold cross-validation to validate the approach.ResultsData of 35,357 individuals were analysed, of which 4,734 experienced falls. Our NLP topic modelling technique discovered 151 topics from the unstructured clinical notes. AUCs and 95% confidence intervals of the Baseline, Topic-based and Combi models were 0.709 (0.700–0.719), 0.685 (0.676–0.694) and 0.718 (0.708–0.727), respectively. All the models showed good calibration.ConclusionsUnstructured clinical notes are an additional viable data source to develop and improve prediction models for falls compared to traditional prediction models, but the clinical relevance remains limited.