Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Performance of Motor Imagery-Based Brain–Computer Interface in Poorly Performing Subjects Using a Hybrid-Imagery Method Utilizing Combined Motor and Somatosensory Activity

The phenomena of brain-computer interface-inefficiency in transfer rates and reliability can hinder development and use of brain-computer interface technology. This study aimed to enhance the classification performance of motor imagery-based brain-computer interface (three-class: left hand, right hand, and right foot) of poor performers using a hybrid-imagery approach that combined motor and somatosensory activity. Twenty healthy subjects participated in these experiments involving the following three paradigms: (1) Control-condition: motor imagery only, (2) Hybrid-condition I: combined motor and somatosensory stimuli (same stimulus: rough ball), and (3) Hybrid-condition II: combined motor and somatosensory stimuli (different stimulus: hard and rough, soft and smooth, and hard and rough ball). The three paradigms for all participants, achieved an average accuracy of 63.60± 21.62%, 71.25± 19.53%, and 84.09± 12.79% using the filter bank common spatial pattern algorithm (5-fold cross-validation), respectively. In the poor performance group, the Hybrid-condition II paradigm achieved an accuracy of 81.82%, showing a significant increase of 38.86% and 21.04% in accuracy compared to the control-condition (42.96%) and Hybrid-condition I (60.78%), respectively. Conversely, the good performance group showed a pattern of increasing accuracy, with no significant difference between the three paradigms. The Hybrid-condition II paradigm provided high concentration and discrimination to poor performers in the motor imagery-based brain-computer interface and generated the enhanced event-related desynchronization pattern in three modalities corresponding to different types of somatosensory stimuli in motor and somatosensory regions compared to the Control-condition and Hybrid-condition I. The hybrid-imagery approach can help improve motor imagery-based brain-computer interface performance, especially for poorly performing users, thus contributing to the practical use and uptake of brain-computer interface.
Discrete-vortex method with novel shedding criterion for unsteady aerofoil flows with intermittent leading-edge vortex shedding

Abstract Unsteady aerofoil flows are often characterized by leading-edge vortex (LEV) shedding. While experiments and high-order computations have contributed to our understanding of these flows, fast low-order methods are needed for engineering tasks. Classical unsteady aerofoil theories are limited to small amplitudes and attached leading-edge flows. Discrete-vortex methods that model vortex shedding from leading edges assume continuous shedding, valid only for sharp leading edges, or shedding governed by ad-hoc criteria such as a critical angle of attack, valid only for a restricted set of kinematics. We present a criterion for intermittent vortex shedding from rounded leading edges that is governed by a maximum allowable leading-edge suction. We show that, when using unsteady thin aerofoil theory, this leading-edge suction parameter (LESP) is related to the $\def \xmlpi #1{}\def \mathsfbi #1{\boldsymbol {\mathsf {#1}}}\let \le =\leqslant \let \leq =\leqslant \let \ge =\geqslant \let \geq =\geqslant \def \Pr {\mathit {Pr}}\def \Fr {\mathit {Fr}}\def \Rey {\mathit {Re}}A_0$ term in the Fourier series representing the chordwise variation of bound vorticity. Furthermore, for any aerofoil and Reynolds number, there is a critical value of the LESP, which is independent of the motion kinematics. When the instantaneous LESP value exceeds the critical value, vortex shedding occurs at the leading edge. We have augmented a discrete-time, arbitrary-motion, unsteady thin aerofoil theory with discrete-vortex shedding from the leading edge governed by the instantaneous LESP. Thus, the use of a single empirical parameter, the critical-LESP value, allows us to determine the onset, growth, and termination of LEVs. We show, by comparison with experimental and computational results for several aerofoils, motions and Reynolds numbers, that this computationally inexpensive method is successful in predicting the complex flows and forces resulting from intermittent LEV shedding, thus validating the LESP concept.
A Novel Algorithm for Classifying Measuring Curves Based on Interval Estimation

Due to noises and instrument defects, frequency-or time-domain measuring curves of a certain signal are usually different. It is then of great importance to recognize which signal a measuring curve belongs to, resulting in a classification problem. Considering the slow running speed of the commonly used classifier SVM, we propose a novel classification algorithm based on interval estimation. The complexity of our algorithm is linear to the class number. Experiment results demonstrate the feasibility and efficiency of the proposed algorithm.
A Bayesian Population Model with Hierarchical Mixture Priors Applied to Blood Count Data

Abstract Population pharmacokinetic and pharmacodynamic studies require analyzing nonlinear growth curves fit to multiple measurements from study subjects. We propose a class of nonlinear population models with nonparametric second-stage priors for analyzing such data. The proposed models apply a flexible class of mixtures to implement the nonparametric second stage. The discussion is based on a pharmacodynamic study involving longitudinal data consisting of hematologic profiles (i.e., blood counts measured over time) of cancer patients undergoing chemotherapy. We describe a full posterior analysis in a Bayesian framework. This includes prediction of future observations (profiles and end points for new patients), estimation of the mean response function for observed individuals, and inference on population characteristics. The mixture model is specified and given a hyperprior distribution by means of a Dirichlet processes prior on the mixing measure. Estimation is implemented by a combination of various M.
Determination of ionic abundances in the Io torus using the Hopkins Ultraviolet Telescope

Attention is given to a 415-1864-A spectrum of the emissions from the Io torus with about 3-A resolution in first order (830-1864 A) obtained with HUT in December 1990. A list of the 31 strongest features shows that, apart from five features that are totally or partially terrestrial, all are due to O(+), S(+), S(2+), and S(3+). There is no evidence for elements other than oxygen and sulfur. The spectrum shows many additional weaker emission features so that the existence of minor species cannot be ruled out at this time. Because the HUT spectrum contains well-resolved lines for all four of the major species, the mixing ratio (Ni/ne) for each one can be determined without using models of the spectral emissions, or a detailed geometric model of the torus plasma. The relative concentration of O(+) determined from the HUT observation is 38 percent less than that obtained from the Voyager UVS data.
Guaranteed Local Maximum Likelihood Detection of a Change Point in Nonparametric Logistic Regression

We consider nonparametric logistic regression and propose a generalized likelihood test for detecting a threshold effect that indicates a relationship between some risk factor and a defined outcome above the threshold but none below it. One important field of application is occupational medicine and in particular, epidemiological studies. In epidemiological studies, segmented fully parametric logistic regression models are often threshold models, where it is assumed that the exposure has no influence on a response up to a possible unknown threshold, and has an effect beyond that threshold. Finding efficient methods for detection and estimation of a threshold is a very important task in these studies. This article proposes such methods in a context of nonparametric logistic regression. We use a local version of unknown likelihood functions and show that under rather common assumptions the asymptotic power of our test is one. We present a guaranteed non asymptotic upper bound for the significance level of the proposed test. If applying the test yields the acceptance of the conclusion that there was a change point (and hence a threshold limit value), we suggest using the local maximum likelihood estimator of the change point and consider the asymptotic properties of this estimator.
Evaluation of a stand-alone computer-aided detection system for acute intra-cranial hemorrhage in emergency environments

Acute intra-cranial hemorrhage (AIH) may result from traumatic brain injury (TBI). Successful management of AIH depends heavily on the speed and accuracy of diagnosis. Timely diagnosis in emergency environments in both civilian and military settings is difficult primarily due to severe time restraints and lack of resources. Often, diagnosis is performed by emergency physicians rather than trained radiologists. As a result, added support in the form of computer-aided detection (CAD) would greatly enhance the decision-making process and help in providing faster and more accurate diagnosis of AIH. This paper discusses the implementation of a CAD system in an emergency environment, and its efficacy in aiding in the detection of AIH.
Linear and nonlinear acoustic Bloch wave propagation in periodic waveguides

Abstract : Propagation of acoustic waves (linear arid nonlinear, time harmonic and pulsed) in a broad class of periodically nonuniform wave guides is investigated theoretically and experimentally. It is shown that the linear, time harmonic solution wave functions are Bloch wave functions. Expressions for parameters characterizing Bloch waves (such as the Bloch wave number) are derived and the features of their band structure determined. Propagation of linear Bloch wave pulses is investigated using the standard dispersion integral. Several new dispersive pulse solutions exhibiting highly unusual behavior (such as acceleration, carrier frequency shifting, and near infinite group velocity) are found. In the case of nonlinear time harmonic Bloch wave propagation, a forward traveling fundamental Bloch wave generates both forward and backward traveling second harmonic Bloch waves, the amplitudes of which oscillate with distance. An effective coefficient of nonlinearity for Bloch waves is identified and found to be, dependent upon frequency, either larger or smaller than that of the host fluid. The dispersion associated with Bloch waves provides an effective means of suppressing the waveform distorting effect of nonlinearity. These findings are verified with measurements made in an air filled, rectangular aluminum duct loaded with a periodic array of scattering side branches.
Electrostatic instabilities in plasmas with two electron components

This paper considers the linear theory of electrostatic Vlasov instabilities driven by the relative drift between two Maxwellian electron components in an unmagnetized, homogeneous plasma bearing zero current. The dispersion properties, threshold drift speeds, and growth rates of the electron beam instability, the conventional ion acoustic instability, and the ion acoustic beam instability are compared in detailed parametric studies. A new way of illustrating the parameter regimes in which each of these three instabilities has the lowest threshold drift speed is demonstrated. This leads to clearly illustrated criteria for determining when enhanced electrostatic fluctuations between the ion and electron plasma frequencies may be observed in plasmas with two electron components. In the case of a hot beam this criterion is a beam density n/sub b/ which satisfies 0.05< or approx. =n/sub b//n/sub e/< or approx. =0.30 where n/sub e/ is the total electron density.
Kinematics of AWM and MKW Poor Clusters

We have measured 1365 redshifts to a limiting magnitude of R ∼ 15.5 in 15 AWM/MKW clusters and have collected another 203 from the literature in MKW 4s, MKW 2, and MKW 2s. In AWM 7 we have extended the redshift sample to R ∼ 18 in the cluster center. We have identified 704 cluster members in 17 clusters; 201 are newly identified. We summarize the kinematics and distributions of the cluster galaxies and provide an initial discussion of substructure, mass and luminosity segregation, spectral segregation, velocity-dispersion profiles, and the relation of the central galaxy to global cluster properties. We compute optical mass estimates, which we compare with X-ray mass determinations from the literature. The clusters are in a variety of dynamical states, reflected in the three classes of behavior of the velocity-dispersion profile in the core: rising, falling, or flat/ambiguous. The velocity dispersion of the emission-line galaxy population significantly exceeds that of the absorption-line galaxies in almost all of the clusters, and the presence of emission-line galaxies at small projected radii suggests continuing infall of galaxies onto the clusters. The presence of a cD galaxy does not constrain the global cluster properties; these clusters are similar to other poor clusters that contain no cD. We use the similarity of the velocity-dispersion profiles at small radii and the cD-like galaxies' internal velocity dispersions to argue that cD formation is a local phenomenon. Our sample establishes an empirical observational baseline of poor clusters for comparison with simulations of similar systems.
Local Dependence Indexes for Item Pairs Using Item Response Theory

Four statistics are proposed for the detection of local dependence (LD) among items analyzed using item response theory. Among them, the X2 and G2 LD indexes are of special interest. Simulated data are used to study the distribution and sensitivity of these statistics under the null condition, as well as under conditions in which LD is introduced. The results show that under the null condition of local independence, both the X2 and G2 LD indexes have distributions very similar to the X2 distribution with 1 degree of freedom. Under the locally dependent conditions, both indexes appear to be sensitive in detecting LD or multidimensionality among items. When compared to Q3, another statistic often used to detect LD, these new statistics are somewhat less powerful for underlying LD, equally powerful for surface LD, and better behaved in the null case.
Formation of NaNbO3-based conductive crystal lines on glass surface by Yb fiber laser irradiation

NaNbO3-based conductive crystal lines composed of closely compacted fine particles were patterned on the 24.7Na2O–18.8Nb2O5–5Al2O3–2NiO–49.5SiO2 (mol %) glass surface by a continuous wave Yb fiber laser irradiation (λ=1078nm) using the nickel atom heat processing method. Nickel (II) ion was proved to be an efficient optical active absorbent for Yb fiber laser irradiation. Homogeneous and smooth crystal lines with a uniform width of 40μm were obtained with a laser power of 1.1W. X-ray diffraction, micro-Raman spectra, optical microscope, and impedance measurements were used to characterize the laser patterned glass. The conductivity value of the laser patterned glass was nearly two times as large as that of the glass matrix due to the formation of NaNbO3-based conductive crystal lines by Yb laser irradiation.
Polyhedral object recognition using view densities

A method of representing the features of a polyhedral object based on probability density functions defined on observed features is presented. The measured lengths of the edges of a polyhedral object depend on the observation angle. The measured lengths of a combination of adjacent edges in an image have characteristic relationships and associated probabilities of observation. These facts are used to develop a decision scheme for identifying polyhedral objects. Results of experiments are also presented. <<ETX>>
Relativistic cross-focusing of extraordinary and ordinary modes in a magnetoactive plasma

Abstract This paper presents the effect of self-focusing on a circularly polarized beam propagating along the static magnetic field when the extraordinary and ordinary modes are present simultaneously for relativistic intensities. The nonlinearity in the dielectric function arises on account of the relativistic variation of mass, which leads to the mutual coupling of the two modes that support the self-focusing of each other. The propagation and focusing of the first mode affects the propagation and focusing of the second mode. The fact that the two modes are laser-intensity dependent leads to cross-focusing. Dynamics of one laser beam affects the dynamics of the second laser beam. When both the beams or modes are strong, the nonlinearities introduced by the relativistic effect in the presence of the magnetic field are additive in nature, such that one beam can undergo oscillatory self-focusing and other beam simultaneously defocusing and vice versa. The dynamical equation governing the cross-focusing has been set up and a numerical solution has been presented for typical relativistic laser–plasma parameters from a slightly underdense to overdense plasma.
A novel characterization scheme for organic field-effect transistors

We present a novel organic field-effect transistor (OFET) characterization scheme that allows for the rapid measurement of both, mobility and threshold voltage, in OFETs. The scheme uses a simple measurement algorithm and is based on cheap, standard electronic components. Results agree excellently with conventional characterization, even for moderate mobilities. The algorithm can be easily automated, which is believed to make it the method of choice where fast response is required, e.g. when OFETs are used as sensors.
Proteoglycan SPOCK1 as a Poor Prognostic Marker Promotes Malignant Progression of Clear Cell Renal Cell Carcinoma via Triggering the Snail/Slug-MMP-2 Axis-Mediated Epithelial-to-Mesenchymal Transition

Sparc/osteonectin, cwcv, and kazal-like domains proteoglycan 1 (SPOCK1) has been reported to play an oncogenic role in certain cancer types; however, the role of SPOCK1 in the progression of clear cell renal cell carcinoma (ccRCC) remains elusive. Here, higher SPOCK1 transcript and protein levels were observed in ccRCC tissues compared to normal tissues and correlated with advanced clinical stages, larger tumor sizes, and lymph node and distal metastases. Knockdown and overexpression of SPOCK1 in ccRCC cells led to decreased and increased cell clonogenic and migratory/invasive abilities in vitro as well as lower and higher tumor growth and invasion in vivo, respectively. Mechanistically, the gene set enrichment analysis (GSEA) database was used to identify the gene set of epithelial-to-mesenchymal transition (EMT) pathways enriched in ccRCC samples with high SPOCK1 expression. Further mechanistic investigations revealed that SPOCK1 triggered the Snail/Slug–matrix metalloproteinase (MMP)-2 axis to promote EMT and cell motility. Clinical ccRCC samples revealed SPOCK1 to be an independent prognostic factor for overall survival (OS), and positive correlations of SPOCK1 with MMP-2 and mesenchymal-related gene expression levels were found. We observed that patients with SPOCK1high/MMP2high tumors had the shortest OS times compared to others. In conclusion, our findings reveal that SPOCK1 can serve as a useful biomarker for predicting ccRCC progression and prognosis, and as a promising target for treating ccRCC.
Performance of superconducting storage ring cavities at 1500 MHz

Application of superconducting cavities in high energy storage rings offers the advantage of considerable power savings over normal conducting structures. The fabrication, processing and performance of three five-cell, elliptically shaped 1500 MHz storage ring cavities, which have been built from reactor grade niobium and which are equipped with fundamental mode power couplers and higher order mode couplers, are described. Q-values of 5 × 109at 2.1°K and accelerating gradients of 9 MV/m have been achieved using commercial grade niobium. The use of higher thermal conductivity niobium offers the possibility of thermally stabilizing the material and increasing the tolerable power dissipation. With niobium of three times the thermal conductivity of reactor grade material accelerating gradients up to E acc = 15.3 MV/m corresponding to peak magnetic surface fields of 71.6 mT have been achieved.
Design of constellation labelling maps for iteratively demapped modulation schemes based on the assumption of hard-decision virtual channels

A bit-interleaved coded modulation system can be regarded as a serially concatenated (SC) system with a modulation scheme as a full rate inner code. The labelling map of the modulation scheme is very important for the performance of iterative demapping and decoding, which is usually designed with the help of the extrinsic information transfer (EXIT) chart. In the paper an analytical approximate expression for the EXIT function (EF) of a maximum a posterior probability demapper over memoryless channels is derived, where the real channel is replaced with a hard-decision virtual channel to make the EF easy to be calculated. The hard-decision virtual channel is selected to have the same capacity as the real channel. Then a closed-form EXIT function is defined for each signal point, i.e. the single signal EXIT function (SSEF). A useful property is proved that the area under the SSEF for each signal is determined by the constellation itself and has nothing to do with the labelling map. Based on the property and the binary switch algorithm, a general method is proposed to design the labelling maps of modulation schemes with desirable EFs matching channel codes. SC systems can then be built with the desired convergence and asymptotic behaviour.
AI-Driven Validation of Digital Agriculture Models

Digital agriculture employs artificial intelligence (AI) to transform data collected in the field into actionable crop management. Effective digital agriculture models can detect problems early, reducing costs significantly. However, ineffective models can be counterproductive. Farmers often want to validate models by spot checking their fields before expending time and effort on recommended actions. However, in large fields, farmers can spot check too few areas, leading them to wrongly believe that ineffective models are effective. Model validation is especially difficult for models that use neural networks, an AI technology that normally assesses crops health accurately but makes inexplicable recommendations. We present a new approach that trains random forests, an AI modeling approach whose recommendations are easier to explain, to mimic neural network models. Then, using the random forest as an explainable white box, we can (1) gain knowledge about the neural network, (2) assess how well a test set represents possible inputs in a given field, (3) determine when and where a farmer should spot check their field for model validation, and (4) find input data that improve the test set. We tested our approach with data used to assess soybean defoliation. Using information from the four processes above, our approach can reduce spot checks by up to 94%.