bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect and optimization of the Zn3P2 back surface field on the efficiency of CZTS/CZTSSe tandem solar cell: a computational approach
Authors: Bibi Baseerat , Farhadi Bita , Khan Asghar H M Noor Ul Huda , Rahman Waseem Ur , Liu Aimin 
Published: October 2022

Abstract
Copper zinc tin sulfide (CZTS) and copper zinc tin sulfo selenide (CZTSSe) kesterite minerals are nontoxic and abundant in the earth with the promise of cost-effective photovoltaic applications. This study explains a tandem solar cell made of kesterite materials that can capture sunlight over a broad spectrum. The proposed tandem structure comprises a wide bandgap CZTS thin-film upper subcell and an underlying narrow bandgap CZTSSe-based lower subcell. The upper and lower subcells are simulated using solar cell capacitance simulator-one dimension and validated against the experimental results to determine the viability of the proposed tandem design. Additionally, the performance of both subcells is evaluated for tandem configuration by adding a back surface field layer, a modification of the back contact, testing at different thicknesses, and doping of both subcell absorber layers. As a result, optimizing both subcells improved tandem solar cell open-circuit voltage (V
oc) to a maximum of 1.5 V, which led to an efficiency of 23.99% at the current matching condition. Furthermore, how light radiation power and temperature variations impact the proposed solar cell performance is being investigated. This study provides significant insights into efficient tandem solar cell design and manufacture.
Spin-transfer and spin-orbit torques in the Landau–Lifshitz–Gilbert equation
Authors: Meo Andrea , Cronshaw Carenza E , Jenkins Sarah , Lees Amelia , Evans Richard F L 
Published: October 2022

Abstract
Dynamic simulations of spin-transfer and spin-orbit torques are increasingly important for a wide range of spintronic devices including magnetic random access memory, spin-torque nano-oscillators and electrical switching of antiferromagnets. Here we present a computationally efficient method for the implementation of spin-transfer and spin-orbit torques within the Landau–Lifshitz–Gilbert equation used in micromagnetic and atomistic simulations. We consolidate and simplify the varying terminology of different kinds of torques into a physical action and physical origin that clearly shows the common action of spin torques while separating their different physical origins. Our formalism introduces the spin torque as an effective magnetic field, greatly simplifying the numerical implementation and aiding the interpretation of results. The strength of the effective spin torque field unifies the action of the spin torque and subsumes the details of experimental effects such as interface resistance and spin Hall angle into a simple transferable number between numerical simulations. We present a series of numerical tests demonstrating the mechanics of generalised spin torques in a range of spintronic devices. This revised approach to modelling spin-torque effects in numerical simulations enables faster simulations and a more direct way of interpreting the results, and thus it is also suitable to be used in direct comparisons with experimental measurements or in a modelling tool that takes experimental values as input.
Contactless skin perfusion monitoring with video cameras: tracking pharmacological vasoconstriction and vasodilation using photoplethysmographic changes
Authors: Harford M , Villarroel M , Jorge J , Redfern O , Finnegan E , Davidson S , Young J D , Tarassenko L , Watkinson P 
Published: October 2022

Abstract

Objectives. Clinical assessment of skin perfusion informs prognosis in critically ill patients. Video camera monitoring could provide an objective, continuous method to monitor skin perfusion. In this prospective, interventional study of healthy volunteers, we tested whether video camera-derived photoplethysmography imaging and colour measurements could detect drug-induced skin perfusion changes. Approach. We monitored the lower limbs of 30 volunteers using video cameras while administering phenylephrine (a vasoconstrictor) and glyceryl trinitrate (a vasodilator). We report relative pixel intensity changes from baseline, as absolute values are sensitive to environmental factors. The primary outcome was the pre- to peak- infusion green channel amplitude change in the pulsatile PPGi waveform component. Secondary outcomes were pre-to-peak changes in the photoplethysmographic imaging waveform baseline, skin colour hue and skin colour saturation. Main results. The 30 participants had a median age of 29 years (IQR 25–34), sixteen (53%) were male. A 34.7% (p = 0.0001) mean decrease in the amplitude of the pulsatile photoplethysmographic imaging waveform occurred following phenylephrine infusion. A 30.7% (p = 0.000004) mean increase occurred following glyceryl trinitrate infusion. The photoplethysmographic imaging baseline decreased with phenylephrine by 2.1% (p = 0.000 02) and increased with glyceryl trinitrate by 0.5% (p = 0.026). Skin colour hue changed in opposite direction with phenylephrine (−0.0013, p = 0.0002) and glyceryl trinitrate (+0.0006, p = 0.019). Skin colour saturation decreased with phenylephrine by 0.0022 (p = 0.0002), with no significant change observed with glyceryl trinitrate (+0.0005, p = 0.21). Significance. Drug-induced vasoconstriction and vasodilation are associated with detectable changes in photoplethysmographic imaging waveform parameters and skin hue. Our findings suggest video cameras have great potential for continuous, contactless skin perfusion monitoring.
Initial stages of pulsed discharge in saline solutions in a vicinity of threshold voltages
Authors: Korolev Y D , Landl N V , Bolotov A V , Kasyanov V S , Nekhoroshev V O , Shemyakin I A 
Published: October 2022

Abstract
Data are presented on an investigation of pulsed discharges in a solution of 30 g of NaCl per liter of distilled water. The interelectrode gap consists of the active pin electrode and the return plane electrode. The voltage pulse of positive polarity forms by means of an electric circuit where a capacitor bank of 40 μF with an initial voltage V
0 of up to 1500 V is discharged via the gap. In a range of initial voltages V
0 = (550 – 1100) V vapor cavities in a form of the microbubbles are generated near the active electrode and at the electrode surface. The microbubbles are able to merge with each other thus forming macrobubbles and vapor layers, which shield the active electrode from current passage. The process of vapor cavity generation and the effect of shielding are discussed in detail. The discharge at the vapor layer appears at the critical voltage V
0 = 1100 V. The principal prerequisite for discharge arising is that the electrode has to be completely shielded by the vapor layer and the voltage at the layer at the instant of discharge initiation has to be at least at a level of 500 V. However, when the voltage at the gap exceeds the critical value, the condition of complete shielding is not obligatory.
Reverse coating technique for the production of Nb thin films on copper for superconducting radio-frequency applications
Authors: Fonnesu D , Baris A , Calatroni S , Lain Amador L , Pfeiffer S , Rosaz G , Bonura M , Senatore C 
Published: October 2022

Abstract
In the framework of the Future Circular Collider Study, the development of thin-film coated superconducting radio-frequency copper cavities capable of providing higher accelerating fields (10–20 MV m−1 against 5 MV m−1 for the Large Hadron Collider) represents a major challenge. The method investigated here for the production of seamless niobium-coated copper cavities is based on the electroforming of the copper structure around a sacrificial aluminium mandrel that is pre-coated with a niobium thin film. The first feasibility study, applied to a flat aluminium disk mandrel, is presented. Protective precautions are taken towards the functional niobium film during the production process and it is shown that this technique can deliver well performing niobium films on a seamless copper substrate. This way, the non-trivial chemical treatments foreseen by the standard procedures (e.g. SUBU, EP) for the preparation of the copper surface to achieve the proper adhesion of the niobium layer are also avoided. The only major chemical treatment involved in the reverse-coating method is represented by the chemical dissolution of the aluminium mandrel, which has the advantage of not affecting the copper substrate and therefore the copper-niobium interface.
Effect of hexagonality on the pressure-dependent lattice dynamics of 4H-SiC
Authors: Zhang Junran , Liang Tao , Lu Yunhao , Xu Binjie , Deng Tianqi , Zhang Yiqiang , Zeng Zhidan , Pi Xiaodong , Yang Deren , Wang Rong 
Published: October 2022

Abstract
The pressure-dependent lattice dynamics of 4H-SiC is investigated using diamond anvil cell, and compared with those of 3C- and 6H-SiC. It is found that both the zone-center longitudinal optical (LO) and transverse optical (TO) modes shift to higher frequencies with the increase of the applied pressures. This indicates that polymorph transitions are unlikely to happen under the (quasi-)hydrostatic pressure. The LO–TO splitting is described well by the cubic function with respect to the applied pressure. A decrease in the LO–TO splitting is observed above 33 GPa. The change of transverse effective charge and thus the ionic character of 4H-SiC exhibits a cubic dependence on the pressure due to the nonequivalent lattice dynamics parallel and perpendicular to the c-axis of 4H-SiC. Compared to what happens in 6H-SiC, the high pressure exerts higher effect on the ionic character of 4H-SiC because less nonequivalent bilayers are evolved. At last, the mode-Grüneisen parameters of the LO and TO modes at the Γ point are determined. Given the hexagonal lattice of 4H-SiC, the LO mode are softer than the TO mode.
Tunable spatial Goos-Hänchen shift in periodic PT-symmetric photonic crystals with a central defect
Authors: Zhang Xiaoping , Zeng Xiangjin , Zhou Guopeng , Ni Hao 
Published: October 2022

Abstract
The Goos-Hänchen (GH) effects are investigated for a transverse magnetic beam totally reflected by periodic PT-symmetric photonic crystals. A dielectric layer with a real refractive index situates as a defect in the center. Both positive and negative spatial GH shifts are induced around the defect mode. The GH shifts sensitively change with the refractive index of the defect layer. More importantly, giant negative GH shift effect with big reflectance could be found and modulated by increasing the gain-loss factor in PT-symmetric systems, which could improve the detecting sensitivity of the defect. Our study may be valuable in potential applications for highly sensitive sensors.
Magnetohydrodynamic instabilities in a self-gravitating rotating cosmic plasma
Authors: Turi Jyoti , Misra A P 
Published: October 2022

Abstract
The generation of magnetohydrodynamic (MHD) waves and their instabilities are studied in galactic gaseous rotating plasmas with the effects of the magnetic field, the self gravity, the diffusion-convection of cosmic rays as well as the gas and cosmic-ray pressures. The coupling of the Jeans, Alfvén and magnetosonic waves, and the conditions of damping or instability are studied in three different cases, namely when the propagation direction is perpendicular, parallel and oblique to the static magnetic field, and are shown to be significantly modified by the effects of the Coriolis force due to the rotation of cosmic fluids and the cosmic-ray diffusion. The coupled modes can be damped or anti-damped depending on the wave number is above or below the Jeans critical wave number that is reduced by the effects of the Coriolis force and the cosmic-ray pressure. It is found that the deviation of the axis of rotation from the direction of the static magnetic field gives rise to the coupling between the Alfvén wave and the classical Jeans mode which otherwise results into the modified slow and fast Alfvén waves as well as the modified classical Jeans modes. Furthermore, due to the effects of the cosmic rays diffusion, there appears a new wave mode (may be called the fast Jeans mode) in the intermediate frequency regimes of the slow and fast Alfvén waves, which seems to be dispersionless in the long-wavelength propagation and has a lower growth rate of instability in the high density regimes of galaxies. The dispersion properties and the instabilities of different kinds of MHD waves reported here can play pivotal roles in the formation of various galactic structures at different length scales.
Field- and anisotropy-induced magnetic transitions and specific heat of one-dimensional XYZ ferromagnet with single-ion anisotropy
Authors: Pham Thao Huong 
Published: October 2022

Abstract
Functional integral method is used to study the field- and anisotropy-induced magnetic transitions, spin waves and the specific heat of a one-dimensional spin-S XYZ ferromagnet with the addition of the single-ion anisotropy in an oblique magnetic field. Field-induced critical lines that begin at quantum critical points and depend on the anisotropy are found. Besides, a double-peak structure in temperature-dependent behavior of the specific heat is observed. The position and height of these peaks can be adjusted by the anisotropy and by the magnetic field. From these results, the important role of thermal and quantum fluctuations is also discussed. Finally, we comment on comparing our results with experimental and other theoretical studies on quasi-one-dimensional anisotropic ferromagnets.
Bispectrum-window convolution via Hankel transform
Authors: Pardede Kevin , Rizzo Federico , Biagetti Matteo , Castorina Emanuele , Sefusatti Emiliano , Monaco Pierluigi 
Published: October 2022

Abstract
We present a method to perform the exact convolution of the model prediction for
bispectrum multipoles in redshift space with the survey window function. We extend a widely
applied method for the power spectrum convolution to the bispectrum, taking advantage of a
2D-FFTlog algorithm. As a preliminary test of its accuracy, we consider the toy model of a
spherical window function in real space. This setup provides an analytical evaluation of the
3-point function of the window, and therefore it allows to isolate and quantify possible
systematic errors of the method. We find that our implementation of the convolution in terms of a
mixing matrix shows differences at the percent level in comparison to the measurements from a very
large set of mock halo catalogs. It is also able to recover unbiased constraints on halo bias
parameters in a likelihood analysis of a set of numerical simulations with a total volume of
100 h
-3 Gpc3. For the level of accuracy required by these tests, the multiplication with the mixing
matrix is performed in the time of one second or less.
Improving automated diagnosis of epilepsy from EEGs beyond IEDs
Authors: Thangavel Prasanth , Thomas John , Sinha Nishant , Peh Wei Yan , Yuvaraj Rajamanickam , Cash Sydney S , Chaudhari Rima , Karia Sagar , Jing Jin , Rathakrishnan Rahul , Saini Vinay , Shah Nilesh , Srivastava Rohit , Tan Yee-Leng , Westover Brandon , Dauwels Justin 
Published: October 2022

Abstract

Objective. Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs. Approach. In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India. Main results. In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs. Significance. These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.
Subject adaptation convolutional neural network for EEG-based motor imagery classification
Authors: Liu Siwei , Zhang Jia , Wang Andong , Wu Hanrui , Zhao Qibin , Long Jinyi 
Published: October 2022

Abstract

Objective. Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted. Approach. Here, we propose a novel end-to-end deep subject adaptation convolutional neural network (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e. a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy. By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject. Main results. Extensive experiments are carried out on three EEG-based MI datasets, i.e. brain–computer interface (BCI) competition IV dataset IIb, BCI competition III dataset IVa, and BCI competition IV dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN. Significance. This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.
Forecasting psychogenic non-epileptic seizure likelihood from ambulatory EEG and ECG
Authors: Xiong Wenjuan , Kameneva Tatiana , Lambert Elisabeth , Cook Mark J , Richardson Mark P , Nurse Ewan S 
Published: October 2022

Abstract

Objective. Critical slowing features (variance and autocorrelation) of long-term continuous electroencephalography (EEG) and electrocardiography (ECG) data have previously been used to forecast epileptic seizure onset. This study tested the feasibility of forecasting non-epileptic seizures using the same methods. In doing so, we examined if long-term cycles of brain and cardiac activity are present in clinical physiological recordings of psychogenic non-epileptic seizures (PNES). Approach. Retrospectively accessed ambulatory EEG and ECG data from 15 patients with non-epileptic seizures and no background of epilepsy were used for developing the forecasting system. The median period of recordings was 161 h, with a median of 7 non-epileptic seizures per patient. The phases of different cycles (5 min, 1 h, 6 h, 12 h, 24 h) of EEG and RR interval (RRI) critical slowing features were investigated. Forecasters were generated using combinations of the variance and autocorrelation of both EEG and the RRI of the ECG at each of the aforementioned cycle lengths. Optimal forecasters were selected as those with the highest area under the receiver-operator curve (AUC). Main results. It was found that PNES events occurred in the rising phases of EEG feature cycles of 12 and 24 h in duration at a rate significantly above chance. We demonstrated that the proposed forecasters achieved performance significantly better than chance in 8/15 of patients, and the mean AUC of the best forecaster across patients was 0.79. Significance. To our knowledge, this is the first study to retrospectively forecast non-epileptic seizures using both EEG and ECG data. The significance of EEG in the forecasting models suggests that cyclic EEG features of non-epileptic seizures exist. This study opens the potential of seizure forecasting beyond epilepsy, into other disorders of episodic loss of consciousness or dissociation.
Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain–computer interfaces
Authors: Sosulski Jan , Tangermann Michael 
Published: October 2022

Abstract

Objective. Covariance matrices of noisy multichannel electroencephalogram (EEG) time series data provide essential information for the decoding of brain signals using machine learning methods. However, small datasets and high dimensionality make it hard to estimate these matrices. In brain–computer interfaces (BCI) based on event-related potentials (ERP) and a linear discriminant analysis (LDA) classifier, the state of the art covariance estimation uses shrinkage regularization. As this is a general covariance regularization approach, we aim at improving LDA further by better exploiting the domain-specific characteristics of the EEG to regularize the covariance estimates. Approach. We propose to enforce a block-Toeplitz structure for the covariance matrix of the LDA, which implements an assumption of signal stationarity in short time windows for each channel. Main results. An offline re-analysis of data collected from 213 subjects under 13 different event-related potential BCI protocols showed a significantly increased binary classification performance of this ‘ToeplitzLDA’ compared to shrinkage regularized LDA (up to 6 AUC points, p < 0.001) and Riemannian classification approaches (up to 2 AUC points, p < 0.001). In an unsupervised visual speller application, this improvement would translate to a relative reduction of spelling errors by 81% on average for 25 subjects. Additionally, aside from lower memory and reduced time complexity for LDA training, ToeplitzLDA proves to be robust against drastic increases of the number of temporal features. Significance. The proposed covariance estimation allows BCI researchers to improve classification rates and reduce calibration times of BCI protocols using event-related potentials and thus support the usability of corresponding applications. Its lower computational and memory needs could make it a valuable algorithm especially for mobile BCIs.