Miracle-dz/UniFAR-FTUs huggingface dataset:
3D‐printed cylindrical Luneburg lens antenna for millimeter‐wave applications

A 3D‐printed cylindrical Luneburg lens antenna working at 26 GHz is proposed in this article. The antenna consists of a feeding waveguide, a 3D‐printed cylinder, and a pair of printed metal grids which are stuck on the side faces of the cylinder. 3D‐printed structure ensures the convenience for processing and structural integrity of the Luneburg lens. Hole drilling technology is utilized for the design of the cylindrical lens. In the E‐plane, conversion of spherical waves into planar waves is achieved based on the gradient refractive index which is realized by the gradient equivalent relative dielectric constant. The main part of the lens contains a hole drilling region to realize the desired equivalent permittivity from 1.23 to 2, while another gradual‐thickness region realize the permittivity ranges from 1.23 to 1. H‐probe method is utilized for the optimization of the gradual‐thickness region in this article. And for the H‐plane, with the grids, H‐field distribution is optimized compared with the Luneburg lens antenna without the loading grids. Thus, the side lobe level (SLL) in H‐plane could be reduced. Meanwhile, a narrower half power beamwidth (HPBW) in H‐plane will be obtained due to the metal grids. Experiment results illustrate the feasibility and validity of the proposed 3D‐printed cylindrical Luneburg lens antenna.
Spatially Explicit Model for Statistical Downscaling of Satellite Passive Microwave Soil Moisture

We introduce a spatially explicit statistical downscaling (SESD) method that fuses multiscale geospatial data with the soil moisture (SM) product from NASA’s SM Active and Passive (SMAP) satellite. The multiscale data included the 9-km resolution SMAP SM image, 1-km resolution normalized difference vegetation index (NDVI), 1-km digital elevation model (DEM), 1-km resolution MODIS land surface temperature (LST), 500-m resolution gross primary productivity (GPP), 30-m resolution topographical water index (TWI), and West Texas Mesonet (WTM) station data. We used the random forest (RF) machine learning method to make a downscaled SM prediction at the 1-km resolution. Then, a regression kriging was applied to model the unpredicted variability at local scales to produce downscaled SM using the WTM station data. Due to the low number of ground truth samples, the validation was based on Monte -Carlo cross validation (CV) to calculate the unbiased root-mean-square deviation (ubRMSD), root-mean-square deviation (RMSD), and bias of the test set randomly separated from the training set from the WTM station data. Model validation showed that the downscaled SM data at the 1-km resolution can significantly improve the accuracy of the SM product as well as enhancing its spatial resolution. This article has its novelty in using the spatially explicit model to reconcile the scale difference from satellite data and ground observations.
Evaluation of an Enzyme-Immunometric Assay for Serum α-Glutathione S-Transferase

A commercially available enzyme-immunometric assay for serum α-glutathione S-transferase (GST) was evaluated. Endogenous serum α-GST diluted linearly within the calibration range. However, we recommend that the sample and second antibody reagent are always added sequentially in the assay to avoid hook effect. Between-assay variability was below 7% across the calibration range and the upper limit of the reference range in adults (n = 219) was 11 · 4 μg/L. Within-individual variability in serum α-GST concentrations measured over a 4–6 week period in 4 healthy adults was small. Serum α-GST concentrations did not change significantly 6 h after a therapeutic dose of paracetamol. Studies in two patients after liver transplantation showed that serum α-GST is a better discriminant of acute changes in liver function than conventional tests. Serum α-GST concentrations were unaffected by gross muscle damage, extra-hepatic inflammation, or haemolysis and thus appear to be more liver specific than transaminase activities. The effect of renal impairment on serum α-GST concentrations requires further investigation.
Estudo por microscopia/espectroscopia de tunelamento e difração de raios x da correlação entre propriedades estruturais e eletrônicas de grafeno epitaxial sobre cobre

Neste trabalho utilizamos as tecnicas de microscopia e espectroscopia de tunelamento para estabelecer conexoes entre as propriedades estruturais e eletronicas de uma amostra de grafeno epitaxial crescida sobre um substrato de cobre policristalino por deposicao quimica na fase vapor. Em particular, foi possivel correlacionar as alteracoes na estrutura de bandas desse material com uma deformacao periodica induzida pelo substrato. As imagens de microscopia de tunelamento revelaram diretamente o casamento epitaxial entre a monocamada de grafeno e o substrato de cobre em diferentes orientacoes cristalograficas atraves de padroes de Moire distintos. Utilizando a espectroscopia de tunelamento mostramos que a presenca do substrato modifica drasticamente a densidade de estados eletronicos do grafeno e, consequentemente, sua estrutura eletronica de bandas. Em especial, observamos o aparecimento de uma serie de picos nos diagramas de densidade de estados, para energias perto do nivel de Fermi, em diversas regioes da amostra estudada. No sentido de compreender os fenomenos fisicos envolvidos nas observacoes experimentais foram realizados calculos atraves da teoria do funcional da densidade que nos permitiram a construcao de um modelo fenomenologico capaz de explicar o problema. Esse modelo nos permitiu associar os estados eletronicos preferenciais a presenca de deformacoes periodicas reveladas pelos padroes de Moire. Finalmente, utilizamos a tecnica de difracao de raios X por incidencia rasante para corroborarmos o modelo estrutural proposto.
Walsh Fourier Transform of Locally Stationary Time Series

A new time‐frequency model and a method to classify time series data are proposed in this article. By viewing the observed signals as realizations of locally dyadic stationary (LDS) processes, a LDS model can be used to provide a time‐frequency decomposition of the signals, under which the evolutionary Walsh spectrum and related statistics can be defined and estimated. The classification procedure is as follows. First choose a training data set that comprises two groups of time series with a known group. Then compute the time frequency feature (the energy) using the training data set, and use a best tree method to maximize the discrepancy of this feature between the two groups. Finally, choose the testing data set with the unknown group as validation data, and use a discriminant statistic to classify the validation data to one of the groups. The classification method is illustrated via an electroencephalographic dataset and the Ericsson B transaction time dataset. The proposed classification method performs better for integer‐valued time series in terms of classification error rates in both simulations and real‐life applications.
Elliptical monopole based super wideband fractal antenna

An elliptical monopole based third iterative fractal antenna for super wideband applications is investigated in this article. It consists of microstrip line fed three iterations of elliptical monopole in tree shaped manner and modified semielliptical ground plane. It has an impedance bandwidth of 0.65 to 35.61 GHz, that is, ratio bandwidth of 54.78:1. The overall footprint of the antenna is 170 × 150 mm2. A peak realized gain of 6.51 dB with an average gain of 3.24 dB is achieved. The investigated antenna has advantages of miniaturized dimensions, simple geometry, and wider bandwidth over previously reported structures.
Uncovering dwarf elliptical evolution through spatially resolved spectroscopy

Abstract Using spatially resolved spectroscopy from the SDSS-IV Mapping Nearby Galaxies at APO (MaNGA) survey, we identify 69 dwarf elliptical (dE) galaxies in the nearby Universe fainter than Mr = −19 (MB = −18), selected independently of morphology and environment. The majority exhibit coherent rotation in their stellar kinematics, consistent with an origin as morphologically transformed disk galaxies. Six galaxies in this dE sample appear to host Active Galactic Nuclei (AGN) that are likely preventing current star formation through maintenance mode feedback. The ionised gas component of these dEs is typically kinematically offset from the stellar component, suggesting the gas is either recently accreted or outflowing. We therefore demonstrate the potential of IFU spectroscopy for understanding the physical properties of dwarf galaxies in detail.
Gouy phase shift measurement in a high-finesse cavity by optical feedback frequency locking

We demonstrate a simple and robust scheme to measure the relative position of transverse cavity modes with high precision. This is based on robust optical-feedback frequency locking of a diode laser to a cavity resonance. A fraction of the laser power is then frequency shifted and used to re-inject other cavity resonances to obtain their positions relative to the locking resonance. This delivers the Gouy phase shift of the cavity modes with precision down to rad, comparable to previous state of the art based on a more complex setup. This should also be of interest for high precision/accuracy measurement of distance and displacement.
Super-resolution algorithm applied to images acquired at millimeter wave frequency in single pixel and computational ghost imaging configurations

ABSTRACT The lack of low cost, high-resolution two-dimensional imaging detectors and the expensiveness of the measurements make millimeter wave imaging challenging. Single detector configurations are promising to solve data acquisition problem in millimeter wave imaging. In this study, millimeter wave imaging in transmission mode is done with single pixel imaging and computational ghost imaging configurations. In order to reduce the number of measurements in each configuration, Compressed Sensing architecture is used. To increase the image resolution, software-based super-resolution is applied. To the best of our knowledge, this is first study comparing the computational ghost imaging with single pixel imaging, besides software-based super-resolution method is applied to the real world millimeter wave images. It is seen that super-resolution increases the quality in both configurations while computational ghost imaging has better quality compared to single pixel imaging in millimeter waves.
Linearized Difference Networks as Approximate Kernel Methods

In this paper we study neural networks as approximations to a related kernel method. We analyze the predictions made by a vanilla neural network with one hidden layer. Two sources of variance in the predictions are identified and eliminated by introducing Linearized Difference Networks. It is shown that in the case of an infinite ensemble of infinitely wide neural networks (IEIN limit), the predictions will be exactly the same as those produced by a kernel method. We verify empirically that finite Linearized Difference Networks indeed produce predictions closer to this ideal kernel method than a vanilla neural network.
Multi-Feature Weighted Sparse Graph for SAR Image Analysis

Sparse representation (SR) method has the advantages of good category distinguishing performance, noise robustness, and data adaptiveness. In this article, a multi-feature weighted sparse graph (MWSG) is presented for synthetic aperture radar (SAR) image analysis. First, multiple types of features are extracted to fully describe the characteristics of SAR image. Then, multiple SRs of samples in multiple feature spaces are obtained by solving a weighted joint SR model, in which the weight is the Gaussian kernel distance among samples. Moreover, a new fusion mechanism is given to integrate multiple weighted SRs, which aims to eliminate the negative influence of the singular data, so the MWSG is obtained. Afterward, the brief steps of the SAR image segmentation and semisupervised classification based on MWSG are stated. A series of experiments on the simulated and real SAR images shows that the MWSG has better performance than other existing relevant methods.
Event-Driven Fault Detection for Discrete-Time Interval Type-2 Fuzzy Systems

This article considers the event-driven fault detection (FD) problem for discrete-time interval type-2 (IT2) fuzzy networked control systems (NCSs) with nonlinear perturbations. To reduce signal transmission, an event-driven mechanism in the NCSs is introduced. A novel FD fuzzy filter is designed for generating a residual signal and detecting actuator faults by considering the event-driven strategy, nonlinear perturbations, communication delay, and mismatched membership functions. It is shown that the proposed event-driven FD technique is effective to optimize performance compared with the existing FD strategy. Finally, two examples are presented to verify the advantages of the novel design technique.
Altman Z-Score Model Effects of Non-Performing Assets (NPAs) in Public and Private Bank In India

The study aims to measure the effectiveness of Altman's Z-score model using Non-Performing Assets (NPA) as a level fixture index. Paper examines if Altman's Z-score model captures the decline in the financial health of the banks caused by the NPAs. The sample size is taken in selected two public (CBI & PNB) & two private sector banks (ICICI & HDFC). The study is based on secondary data & data will be collected for the last five years 2014 to 2018. The findings of this paper suggest that, due to the uniqueness of the Indian banking sector during the NPA crisis, the 'Emerging Market Model' does not produce any significantly better results. Therefore, there is a future scope to develop a tailor-made model suitable to the Indian Banking sector.
Addressing Linear Regression Models with Correlated Regressors: Some Package Development in R

Addressing Linear Regression Models with Correlated Regressors: Some Package Development in R Muhammad Imdad Ullah Regression analysis is widely used in many elds such as economics, nance, technology and social sciences. A linear regression model describes the relationship between dependent variable and one or more regressors. A problem named multicollinearity is the existence/presence of nearly linear dependency among regressors. The existence of severe multicollinearity questions the accuracy of the parameters estimate in a linear regression model, because the variance of the ordinary least square estimator (OLSE) would be large enough. Therefore, detection of multicollinearity can be considered as rst step for giving solution of this problem. There are several indicators (diagnostic measures) for the quanti cation of collinearity among regressors available in the literature. Widely used and the most suggested diagnostics are values of pair-wise correlations among regressors, overall R, VIF and tolerance limit, eigenvalues, condition number and condition indices etc. However, there is no unique method that can detect and measure the existence and strength of multicollinearity in data. For remedy or reduction of collinearity among regressors, biased estimators are suggested such as the ridge and Liu estimators in literature and are alternative to the OLSE. These alternative methods also improve the accuracy of the parameter estimates of linear regression models. The biased methods resemble each other and are based on similar principals. In this study, we propose two new detection methods for indication of collinearity
Stability analysis and robust synchronization of fractional‐order competitive neural networks with different time scales and impulsive perturbations

This article mainly examine a class of robust synchronization, global stability criterion, and boundedness analysis for delayed fractional‐order competitive type‐neural networks with impulsive effects and different time scales. Firstly, by endowing the robust analysis skills and a new class of Lyapunov‐Krasovskii functional approach, the error dynamical system is furnished to be a robust adaptive synchronization in the voice of linear matrix inequality (LMI) technique. Secondly, by ignoring the uncertain parameter terms, the existence of equilibrium points are established by means of topological degree properties, and the solution representation of the considered network model are provided. Thirdly, a novel global asymptotic stability condition is proposed in the voice of LMIs, which is less conservative. Finally, our analytical results are justified with two numerical examples with simulations.
Analysis of the Evolution of the Structure of a Surface With Pyramidal Asperities in Contact With a Hard and Smooth Plane

This research deals with the evolution of the structure of the sapphire–brass interface due to the variation of contact pressure. This evolution primarily affects the essential parameters that govern the thermal contact resistance (TCR), namely, the contact point density N, the ratio of real area of contact S*, and the distance d separating the median contact planes. The combination of three measurement techniques, namely, profilometry, imaging, and mechanical characterization, was used for the purpose of investigating the structural variation of the interface. Alternatively, the TCR, which prevails at the interface, was estimated. Thus, the object of our study is to propose an original and new experimental approach allowing at the same time the precise measurement of the TCR and the estimate of the contact parameters of the interface studied constituting input data to the theoretical models of TCR. The estimated values given by these last are then compared with those measured. Through this approach, we try to open new ways of experimentation that would tend to reinforce the effort of TCR modeling. The results obtained showed that the roughness parameters Ra and Rq are independent of loading. The roughness Rp, which is considered equal to d, is sensitive to loading and has the same decreasing behavior under the effect of loading. The determination of S*, using the hardness testing, is even more relevant when the effective hardness Hc is considered. Analysis of data for the estimation of the TCR shows that the comparisons with the reference model (Bardon) attest to the relevance of our approach.
An encoder-decoder based grapheme-to-phoneme converter for Bangla speech synthesis

: This paper proposes an encoder-decoder based sequence-to-sequence model for Grapheme-to-Phoneme (G2P) conversion in Bangla (Exonym: Bengali). G2P models are key components in speech recognition and speech synthesis systems as they describe how words are pronounced. Traditional, rule-based models do not perform well in unseen contexts. We propose to adopt a neural machine translation (NMT) model to solve the G2P problem. We used gated recurrent units (GRU) recurrent neural network (RNN) to build our model. In contrast to joint-sequence based G2P models, our encoder-decoder based model has the ﬂexibility of not requiring explicit grapheme-to-phoneme alignment which are not straight forward to perform. We trained our model on a pronunciation dictionary of (approximately) 135,000 entries and obtained a word error rate (WER) of 12.49% which is a signiﬁcant improvement from the existing rule-based and machine-learning based Bangla G2P models.
Characteristics and Formation Mechanisms of Spring SST Anomalies in the South China Sea and Its Adjacent Regions

Characteristics of the springtime sea surface temperature anomalies (SSTAs) in the South China Sea and its adjacent regions (SCSAR), as well as their possible impacts on the Asian and Indo-Pacific climate, were investigated by using multiple datasets. According to the result from an empirical orthogonal function (EOF) analysis on the spring SSTAs in the SCSAR, the dominant pattern is a uniformly warming pattern in the whole SCSAR region. While the second mode is a sandwich pattern with cold SSTA over the central SCSAR centered near 10° N, flanked by warm SSTA over the northern oceans near 25° N and in the subtropics near 10° S. The uniformly warming pattern is associated with the anomalous warming in the Indian Ocean from the preceding autumn to the spring, and the sandwich pattern is mainly caused by the El Niño-Southern Oscillation. In the uniformly warming pattern, rainfall increases in the Meiyu region and decreases over the southern South China Sea (SCS). In the sandwich pattern, the anomalous anticyclone at 850-hPa causes less rainfall in the Philippine Sea, the Marine Continent, and the SCS. The positive rainfall anomalies could be found in the northern SCS and adjacent regions. Associated with the second EOF mode, there is a wave train emitted from the SCSAR to East Asia, northwest Pacific, and North America. The wave train spreads the energy from mid-latitudes to higher latitudes through atmospheric teleconnection, which can even influence the North American atmospheric circulation in spring.
Genetic studies of ascites in broiler populations

Continuous genetic improvement of growth and conformation traits in broiler populations has coincided with an increase in defects in heart and lung function. These defects have led to an increased incidence of metabolic disorders such as ascites (or pulmonary hypertension), a functional hypoxia. The incidence of ascites in well-managed flocks is low, but it nonetheless causes important economic losses to the breeding industry and is an important issue from a welfare standpoint. The aim of this thesis was to study the genetics of ascites-related quantitative traits. A low blood oxygen saturation (SaO) value is a good indicator of ascites susceptibility. The existence of substantial genetic (polygenic) variation for SaO was demonstrated for four meat-type chicken lines. Estimates of heritabilities for SaO ranged from 0.1 to 0.2 and additive genetic correlations with production traits were not different from zero. SaO data from one of these lines were analysed using a mixed inheritance model (i.e., including a major locus and polygenes) and the results suggested that a major locus with two alleles at intermediate frequencies affected SaO. The putative major locus accounted for a difference of 13 % SaO between homozygotes and the decreasing allele was recessive. The major locus was also estimated to have an overdominant effect on weight and fleshing score. The mode of action of the putative major locus on SaO and production traits would hinder manipulation of its allele frequency without the use of molecular markers. A population was designed to map this putative major locus. Power studies were performed to select a number of sires and their half-sib progeny. Sires were selected on the basis of their probability of being heterozygous at the putative major locus as estimated by the segregation analysis. Regions around the three ryanodine receptor loci (RYRI, RYR2 and RYR3), which are candidate genes for ascites, were chosen to perform a linkage study. No evidence of linkage of any of the regions studied with SaO, as a predictor of ascites, was detected. However, strong evidence of a quantitative trait locus for fleshing score linked to the RYR3 region was found. The resource population created for this study could be used to