bluuebunny/crossref_metadata_2025_split huggingface dataset:
1D fluid model of the interaction between helium APPJ and deionized water
Authors: Liu Yifan , Liu Dingxin , Luo Santu , Sun Bowen , Zhang Mingyan , Yang Aijun , Kong Michael G 
Published: March 2022

Abstract
Atmospheric pressure plasma jets (APPJs) are widely used for the treatment of water-containing substances such as human tissue, leading to a necessity of understanding the interaction between APPJs and water solutions for the development of plasma biomedicine. The reported two- or three-dimensional fluid models are shown to be an effective method for this study. However, owing to the complex chemistry in APPJ-water interaction, little of them could provide a quantitative estimation of reactive species, which are difficult to be measured but of much interest in the applications. In this paper, a one-dimensional fluid model is developed to simulate the interaction between a helium APPJ and deionized water, which incorporates a relatively comprehensive chemistry both in gas and liquid phases but with a moderate computational load. The composition and distribution of reactive species are quantified during a plasma treatment time of 6 min, which is typical in practice. By considering the sidewise loss inside the quartz tube, the air mixing outside the quartz tube, the conductivity of deionized water, and the chlorine evolution reaction, the simulation results agree well with the experiments. It is found that the plasma could be divided into three regions with much different physicochemical properties, mainly due to the sidewise loss, the air mixing and the water evaporation. In plasma-activated water, H2O2aq and HNO2aq/NO2aq
− are the dominant reactive species, and OHaq is the key intermediate species for the transformation among other reactive species. Finally, the chemical pathways for the production of aqueous reactive species are elucidated.
Non-contact measurement of human respiration using an infrared thermal camera and the deep learning method
Authors: Shu Shuangbao , Liang Huajun , Zhang Yu , Zhang Yuzhong , Yang Ziqiang 
Published: March 2022

Abstract
The existing methods of contact respiration rate (RR) measurement can bring discomfort to the person being measured. However, RR is a human index that has to be monitored in clinical medicine. To overcome the limitations of contact measurement methods, a non-contact RR measurement method based on an infrared thermal camera is proposed. This is based on the phenomenon that human breathing causes periodic temperature changes around the nostrils. First, an infrared thermal camera is used to collect image sequences of the human face. And then, to track a region-of-interest (ROI) in moving image sequences, the You Only Look Once V3 deep learning method is used to track the nostril ROI from the recorded facial image sequences. The performance of the deep learning model is analyzed through experiments. The average temperature of the nostril ROI of the tracked image is calculated, and the temperature change value of the continuous image series can be obtained. The temperature change curve reflects the respiratory process. A Butterworth low-pass filter is used to filter the continuous temperature value to obtain robust respiratory signals, to improve the signal-noise ratio. Last, the respiratory rate is calculated by time-domain and frequency-domain methods. RR is calculated in different conditions, such as normal respiration, fast respiration, and slow respiration. Through the training, the training accuracy of the model reaches 97.9%. Experimental results show that the method can achieve high precision, and the maximum error is not more than 2%. Those indicate the proposed method can effectively measure the RR. Therefore, the proposed non-contact RR measurement method can be a useful reference for clinical RR measurement and other applications.
Evaluating the applicability of classical and neural network interatomic potentials for modeling body centered cubic polymorph of magnesium
Authors: F Troncoso Javier , Turlo Vladyslav 
Published: March 2022

Abstract
Magnesium (Mg) is one of the most abundant metallic elements in nature and presents attractive mechanical properties in the industry. Particularly, it has a low density and relatively high strength/weight and stiffness/weight ratios, which make it one of the most attractive lightweight metals. However, the huge potential of Mg is restricted by its low ductility, associated with its hexagonal close packed (hcp) structure. This problem can be solved if Mg adopts the body centered cubic (bcc) structure, which is stable at high pressure or in confinement with stiff bcc metals like Nb. Molecular dynamics method is a magnificent tool to study material’s structure and deformation mechanisms at the atomic level, however, requiring accurate interatomic potentials. The majority of the interatomic potentials available in the literature for Mg have only been fitted to the properties of its stable hcp phase. In the present work, we perform systematic study of applicability of currently available Mg potentials to modeling the properties of metastable bcc polymorph of Mg, taking into account cohesive energy curves, elastic constants, stacking fault energies, and phonon dispersion curves. We conclude that the modified embedded atom method (MEAM) potentials are the most suitable for investigating bcc Mg in Mg/Nb nano-composites, while the properties of high-pressure bcc Mg would be better modeled by neural network interatomic potentials after different local atomic environments corresponding to bcc Mg being included into the fitting database.
Cascaded mutual enhancing networks for brain tumor subregion segmentation in multiparametric MRI
Authors: Momin Shadab , Lei Yang , Tian Zhen , Roper Justin , Lin Jolinta , Kahn Shannon , Shu Hui-Kuo , Bradley Jeffrey , Liu Tian , Yang Xiaofeng 
Published: March 2022

Abstract
Accurate segmentation of glioma and its subregions plays an important role in radiotherapy treatment planning. Due to a very populated multiparameter magnetic resonance imaging image, manual segmentation tasks can be very time-consuming, meticulous, and prone to subjective errors. Here, we propose a novel deep learning framework based on mutual enhancing networks to automatically segment brain tumor subregions. The proposed framework is suitable for the segmentation of brain tumor subregions owing to the contribution of Retina U-Net followed by the implementation of a mutual enhancing strategy between the classification localization map (CLM) module and segmentation module. Retina U-Net is trained to accurately identify view-of-interest and feature maps of the whole tumor (WT), which are then transferred to the CLM module and segmentation module. Subsequently, CLM generated by the CLM module is integrated with the segmentation module to bring forth a mutual enhancing strategy. In this way, our proposed framework first focuses on WT through Retina U-Net, and since WT consists of subregions, a mutual enhancing strategy then further aims to classify and segment subregions embedded within WT. We implemented and evaluated our proposed framework on the BraTS 2020 dataset consisting of 369 cases. We performed a 5-fold cross-validation on 200 datasets and a hold-out test on the remaining 169 cases. To demonstrate the effectiveness of our network design, we compared our method against the networks without Retina U-Net, mutual enhancing strategy, and a recently published Cascaded U-Net architecture. Results of all four methods were compared to the ground truth for segmentation and localization accuracies. Our method yielded significantly (P < 0.01) better values of dice-similarity-coefficient, center-of-mass-distance, and volume difference compared to all three competing methods across all tumor labels (necrosis and non-enhancing, edema, enhancing tumor, WT, tumor core) on both validation and hold-out dataset. Overall quantitative and statistical results of this work demonstrate the ability of our method to both accurately and automatically segment brain tumor subregions.
Interbeat interval-based sleep staging: work in progress toward real-time implementation
Authors: Garcia-Molina Gary , Jiang Jiewei 
Published: March 2022

Abstract

Objective. Cardiac activity changes during sleep enable real-time sleep staging. We developed a deep neural network (DNN) to detect sleep stages using interbeat intervals (IBIs) extracted from electrocardiogram signals. Approach. Data from healthy and apnea subjects were used for training and validation; 2 additional datasets (healthy and sleep disorders subjects) were used for testing. R-peak detection was used to determine IBIs before resampling at 2 Hz; the resulting signal was segmented into 150 s windows (30 s shift). DNN output approximated the probabilities of a window belonging to light, deep, REM, or wake stages. Cohen’s Kappa, accuracy, and sensitivity/specificity per stage were determined, and Kappa was optimized using thresholds on probability ratios for each stage versus light sleep. Main results. Mean (SD) Kappa and accuracy for 4 sleep stages were 0.44 (0.09) and 0.65 (0.07), respectively, in healthy subjects. For 3 sleep stages (light+deep, REM, and wake), Kappa and accuracy were 0.52 (0.12) and 0.76 (0.07), respectively. Algorithm performance on data from subjects with REM behavior disorder or periodic limb movement disorder was significantly worse, with Kappa of 0.24 (0.09) and 0.36 (0.12), respectively. Average processing time by an ARM microprocessor for a 300-sample window was 19.2 ms. Significance. IBIs can be obtained from a variety of cardiac signals, including electrocardiogram, photoplethysmography, and ballistocardiography. The DNN algorithm presented is 3 orders of magnitude smaller compared with state-of-the-art algorithms and was developed to perform real-time, IBI-based sleep staging. With high specificity and moderate sensitivity for deep and REM sleep, small footprint, and causal processing, this algorithm may be used across different platforms to perform real-time sleep staging and direct intervention strategies. Novelty & Significance (92/100 words) This article describes the development and testing of a deep neural network-based algorithm to detect sleep stages using interbeat intervals, which can be obtained from a variety of cardiac signals including photoplethysmography, electrocardiogram, and ballistocardiography. Based on the interbeat intervals identified in electrocardiogram signals, the algorithm architecture included a group of convolution layers and a group of long short-term memory layers. With its small footprint, fast processing time, high specificity and good sensitivity for deep and REM sleep, this algorithm may provide a good option for real-time sleep staging to direct interventions.
Electropositive core in electronegative magnetized capacitive radio frequency plasmas
Authors: Wang Li , Vass Máté , Donkó Zoltán , Hartmann Peter , Derzsi Aranka , Song Yuan-Hong , Schulze Julian 
Published: March 2022

Abstract
The magnetized drift-ambipolar (‘m-DA’) electron power absorption mode and a sequence of structural transitions, including the formation of an electropositive core where the electron density is much higher than the negative ion density, are identified in a magnetized capacitive Radio-Frequency (RF) plasma of a strongly electronegative gas, CF4. The m-DA mode is caused by a magnetic enhancement of the bulk electric field due to the attenuation of the electron transport and plasma conductivity across the magnetic field. This leads to the formation of ionization maxima at distinct axial positions and a local trapping of electrons by the magnetic field as a function of its strength.
Influence of voltage and gap distance on the dynamics of the ionization front, plasma dots, produced by nanosecond pulsed discharges at water surface
Authors: Herrmann Antoine , Margot Joëlle , Hamdan Ahmad 
Published: March 2022

Abstract
A streamer discharge is a highly reactive and dynamic non-thermal plasma. It has been used in many applications, including environmental remediation, medicine, and material processing. Although the physics of streamer discharges in gaseous media is well understood, its interaction with a solid and liquid dielectric surfaces remains under investigation, in particular when quantitative data are searched for. In this study, we investigate the influence of voltage amplitude (V
a) and interelectrode air gap distance (d) on the pulsed discharge behavior at the surface of distilled water. Time resolved images show the formation and propagation of plasma dots (ionization front of streamers) at water surface. Because of its stochastical nature, a large number of discharge was performed to address the influence of V
a and d on the number of plasma dots (N
Dots) as well on the charge per dot (Q
Dot). As expected, for a given V
a, the breakdown voltage is found to increase with d. Moreover, N
Dots decreases linearly with d at the rate of ∼1 dot by 200 μm of increase, while the total injected charge decays linearly with a rate of ∼8–9 nC by 200 μm of increase. Based on the measurement of the propagation velocity of the plasma dots and on the estimation of the electric field in the medium, an average mobility of plasma dots of ∼1.5 cm2 Vs−1 is evaluated. From both this value and the instantaneous measured propagation velocity, the temporal evolution of Q
Dot and charge number are determined. The observations reported here are of interest for fundamental studies as well as for applications where well-controlled charge transfer to surfaces is crucial.
Survey, taxonomy, and methods of QCA-based design techniques—part I: digital circuits
Authors: Fazili Mohammad Mudakir , Shah Mohsin Fayaz , Naz Syed Farah , Shah Ambika Prasad 
Published: March 2022

Abstract
Quantum-dot cellular automata (QCA) are a promising, novel nanoscale technology that allow the design of integrated circuits with high speed, low power consumption, and high density. Because of this potential benefit, QCA are chosen as a viable alternative to complementary metal-oxide semiconductor (CMOS) technology. In this paper, we have provided a comprehensive review of various types of digital circuits and modules in QCA nanotechnology. We have discussed circuits such as XOR/XNOR, half and full adder, multiplexers and demultiplexers, comparators, flip-flops, arithmetic and logical unit, and random-access memory. We have shown how these circuits are designed using various methodologies such as different types of cross-overs, multi-layer designs, or using cell-to-cell interaction method with their corresponding advantages and overheads. These logical circuits are compared on the basis of various parameters including cell area, total area, latency, number of cells, energy dissipation, and complexity and are explained starting from the design which is having larger cell count to the current efficient design present in terms of the above parameters.
Survey, taxonomy, and methods of QCA-based design techniques—part II: reliability and security
Authors: Fazili Mohammad Mudakir , Shah Mohsin Fayaz , Naz Syed Farah , Shah Ambika Prasad 
Published: March 2022

Abstract
Quantum-dot cellular automata (QCA) is a new and adroit technology currently under extensive research for post-complementary metal-oxide-semiconductor era VLSI chip design. QCA has promised more reliable, fault-tolerant and secure chip designs. In addition, while analyzing QCA circuits for power and energy dissipation, promising results have been reported that suggest that QCA circuits dissipate significantly less energy and operate very close to the Shannon–von Neumann–Landauer limit. Security is another concern that has led to the development of QCA-based security systems such as physically unclonable functions and true random number generators. In this paper, a survey of different fault-tolerant and QCA-based security circuits is provided, along with a discussion of critical design aspects and parameters of QCA technology.
Smart self-sensory TRC pipes—proof of concept
Authors: Perry Gali , Goldfeld Yiska 
Published: March 2022

Abstract
The paper aims to prove the feasibility of smart concrete pipe systems with integrated monitoring capabilities. The development of such systems is motivated by functional, structural, sustainability and monitoring requirements of underground and buried pipelines with limited accessibility. To answer these challenges, the study adopts the textile reinforced concrete (TRC) technology that allows the production of effective, durable, and lightweight structural elements with integrated monitoring systems. In such systems, by utilizing the electrical conductivity of the carbon rovings, the carbon-based textile can simultaneously serve as the main reinforcement system and as the sensory agent. The proposed hybrid monitoring system aims to detect the occurrence of leakage and to distinguish its severity, which is directly correlated to the structural health. Smart TRC pipes were designed, constructed, and experimentally investigated from structural and sensory points of view. The design considers the multifunctionality of the carbon rovings and the hybrid performance of the textile cage, from both aspects—reinforcement and sensing. An experimental investigation explores the mutual structural, functional, and sensory capabilities of the hybrid system, which reflect and affect each other. It is presented that the strong correlation between the structural-functional and sensory responses reveals an efficient smart TRC pipes. The presented results take a major step toward the realization of the smart TRC concept and exceeded beyond small 1D scale elements to 3D structures.
Sensitivity and bifurcation analysis of pine wilt disease with harmonic mean type incidence rate
Authors: Chawla Sant Ram , Ahmad Saeed , Khan Amir 
Published: March 2022

Abstract
We investigate the characteristics of pine wilt disease by incorporating the harmonic type incident rate in an existing model. We find the threshold number for the model under consideration which plays a vital role in the dynamical behaviour of the system. Stability conditions for the possible stationary states of the system are found in terms of the threshold quantity. Our analysis shows that disease eradicates from the pine trees whenever the threshold quantity assumes values less than unity and persists otherwise. We then study the effect of various parameters of the model upon the spread of the pine wilt disease. The problem presented exhibits bifurcation, an analysis of which is a part of the current work. Under biologically meaningful conditions, we perform numerical simulations to support and verify the analytical findings of this work.
Effects of non-minimal matter-geometry coupling on embedding class-one anisotropic solutions
Authors: Sharif M , Naseer T 
Published: March 2022

Abstract
This paper investigates some particular anisotropic star models in

f
(

,

,

)

gravity, where


=



ω
α



ω
α

. We adopt a standard model

f
(

,

,

)
=

+
ϖ


, where ϖ indicates a coupling constant. We take spherically symmetric spacetime and develop solutions to the modified field equations corresponding to different choices of the matter Lagrangian by applying ‘embedding class-one’ scheme. For this purpose, we utilize

MIT

bag model equation of state and investigate some physical aspects of compact models such as RXJ 1856-37, 4U 1820-30, Cen X-3, SAX J 1808.4-3658 and Her X-I. We use masses and radii of these stars and employ the vanishing radial pressure condition at the boundary to calculate the value of their respective bag constant

B

c

. Further, we fix ϖ = ± 4 to analyze the behavior of resulting state variables, anisotropy, mass, compactness, surface redshift as well as energy bounds through graphical interpretation for each star model. Two different physical tests are performed to check the stability of the developed solutions. We conclude that ϖ = −4 is more suitable choice for the considered modified model to obtain stable structures of the compact bodies.
When does the Schwinger preheating occur?
Authors: Okano So , Fujita Tomohiro 
Published: March 2022

Abstract
When the inflaton couples to photons and amplifies electric fields, charged particles produced via the Schwinger effect can dominate the universe after inflation, which is dubbed as the Schwinger preheating. Using the hydrodynamic approach for the Boltzmann equation, we numerically study two cases, the Starobinsky inflation model with the kinetic coupling and the Watanabe-Kanno-Soda inflation model. The Schwinger preheating is not observed in the latter model but occurs for a sufficiently large inflaton-photon coupling in the first model. We analytically address its condition and derive a general attractor solution of the electric fields. The occurrence of the Schwinger preheating in the first model is determined by whether the electric fields enter the attractor solution during inflation or not.
Search for correlations between host properties and DM
host of fast radio bursts: constraints on the baryon mass fraction in IGM *
Authors: Lin Hai-Nan , Li Xin , Tang Li 
Published: March 2022

Abstract
The application of fast radio bursts (FRBs) as probes for investigating astrophysics and cosmology requires proper modelling of the dispersion measures of the Milky Way (

) and host galaxy (

).

can be estimated using the Milky Way electron models, such as the NE2001 model and YMW16 model. However,

is hard to model due to limited information on the local environment of the FRBs. In this study, using 17 well-localized FRBs, we search for possible correlations between

and the properties of the host galaxies, such as the redshift, stellar mass, star-formation rate, age of galaxy, offset of the FRB site from the galactic center, and half-light radius. We find no strong correlation between

and any of the host properties. Assuming that

is a constant for all host galaxies, we constrain the fraction of the baryon mass in the intergalactic medium today to be

. If we model

as a log-normal distribution, however, we obtain a larger value,

. Based on the limited number of FRBs, no strong evidence for a redshift evolution of

is found.
Using

as a spin polarimeter *
Authors: Wei Dai-Hui , Yang Yong-Xu , Ping Rong-Gang 
Published: March 2022

Abstract
Polarization transfer measurement plays an important role in the search for new physics processes in charmed baryon decays. The measurement of the

decay is suggested as a spin polarimeter. A general description of the decay is developed using Euler angles, and the polarization parameters are derived. Its relationship with parity violation is found using the phenomenological amplitude model. A Monte-Carlo simulation is performed, and the results show that charmed baryon polarization is well determined using a set of Monte-Carlo events with selected asymmetry parameters. The experimental measurement of these asymmetry parameters is suggested.