Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of the identical training process on the functional state of high-level sprint swimmers of both genders

Purpose : to study the effect of an identical training process on the functional state of high-level sprint swimmers of both genders. Material and methods. The heart rate variability indices, central hemodynamic parameters and PWC 170/kg measurements were compared between 86 male and 42 female swimmers with sports qualifications from first-class athlete to Master of Sports International Class, and separately, – between 38 males and 9 females (Masters of Sports and Masters of Sports International Class), 28 males and 16 females (Candidates for Masters of Sports), and 22 males and 17 females (first-class athletes). The results. In the general group (86 males vs. 42 females) only the greater value of PWC 170/kg in males by 12.64 % (p<0.001) was revealed, without any differences in heart rate variability and central hemodynamics. Masters of Soprts and Masters of Sports International Class, as well as Candidates for Masters of Sports, didn't have gender differences in all indicators studied. In first-class athletes, there were no gender differences in heart rate variability and central hemodynamic parameters, except for PWC 170/kg , which in males was greater by 11.27 % (p=0.036). Conclusions. A comparison of 86 male and 42 female swimmers with sports qualifications from first-class athlete to Master of Sports International Class, who differed by age and training experience, showed no significant differences in HRV and central hemodynamic parameters except for PWC 170/kg , which was 12.64 % higher in males (p<0.001). There were no significant differences between all studied indices in 38 male and 9 female swimmers with sports qualifications Master of Sports and Master of Sports International Class, with no differences in age or training experience. Comparison of 28 male and 16 female swimmers with sports qualification Candidate for Master of Sports, not differing by age and training experience showed no significant differences in HRV and central hemodynamic parameters, except for PWC 170/ kg , which was greater in males by 11.27 % (p=0.034). In 22 male and 17 female swimmers with sports qualification first-class athlete, not differing by age (males have greater training experience) there were no differences between all studied indices.
Measurements of the Basal Face Surface Area for Plate Family Ice Crystals

Examinaticn of basal plane surface areas of sectorlike, stellar and dendritic ice crystals found in natural clouds was made. Empirical relationships describing basal plane surface area and diameter have been developed. The results suggest that the surface area of the basal face for certain crystals is nearly constant, and thus independent of the crystal size.
Wealth Distribution, Income Inequality and Financial Inclusion: A Panel Data Analysis

Research and data indicate that wealth inequality is more concentrated than income inequality and that there is a high correlation between both variables. Yet, most empirical studies on the determinants of economic inequality focus on income and not wealth inequality and they rarely examine the nexus between the two variables. Against this backdrop, this paper examines the impact of income inequality on the distribution of wealth using panel data and controlling for the roles of financial inclusion and other potential drivers of wealth inequality. We find evidence that lagged wealth and savings rates increase wealth inequality globally as well as in the developed and developing countries samples. We also find that income inequality and return on deposits are dis-equalizing in developing countries while financial inclusion is equalizing in developed countries. In both the global and developing countries samples, financial inclusion has a negative relationship with wealth inequality, but the coefficients are not statistically significant. The findings of the paper have important policy implications for national efforts to address wealth inequality.
A link-free sparse group variable selection method for single-index model

ABSTRACT For regression problems with grouped covariates, we adapt the idea of sparse group lasso (SGL) [10] to the framework of the sufficient dimension reduction. Assuming that the regression falls into a single-index structure, we propose a method called the sparse group sufficient dimension reduction to conduct group and within-group variable selections simultaneously without assuming a specific link function. Simulation studies show that our method is comparable to the SGL under the regular linear model setting and outperforms SGL with higher true positive rates and substantially lower false positive rates when the regression function is nonlinear. One immediate application of our method is to the gene pathway data analysis where genes naturally fall into groups (pathways). An analysis of a glioblastoma microarray data is included for illustration of our method.
Monotone support vector quantile regression

ABSTRACT Quantile regression (QR) models have received a great deal of attention in both the theoretical and applied statistical literature. In this paper we propose support vector quantile regression (SVQR) with monotonicity restriction, which is easily obtained via the dual formulation of the optimization problem. We also provide the generalized approximate cross validation method for choosing the hyperparameters which affect the performance of the proposed SVQR. The experimental results for the synthetic and real data sets confirm the successful performance of the proposed model.
Formalising Futures and Promises in Viper

Futures and promises are respectively a read-only and a write-once pointer to a placeholder in memory. They are used to transfer information between threads in the context of asynchronous concurrent programming. Futures and promises simplify the implementation of synchronisation mechanisms between threads. Nonetheless they can be error prone as data races may arise when references are shared and transferred. We aim at providing a formal tool to detect those errors. Hence, in this paper we propose a proof of concept by implementing the future/promise mechanism in Viper: a veriﬁcation infrastructure, that provides a way to reason about resource ownership in programs.
A saturation property on ideals

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CONSTRUCTION AND GENERALIZATION OF NEAR-HOMOGENEOUS TOURNAMENTS

A homogeneous tournament is a tournament with 4 t + 3 vertices such that every arc is contained in exactly t +1 cycles of length 3. A generalized class of graph is that of near-homogeneous tournaments with 4 t + 1 vertices, in which every arc is contained in t or t + 1 cycles of length 3. In this paper, we ﬁrstly raise a new method to construct near-homogeneous tournaments with 4 t + 1 vertices. Then, we generalize the deﬁnition of near-homogeneous tournament to those tournaments with an even number of vertices. We prove a few results that support the rationality of our deﬁnition. Finally we construct several inﬁnite classes of near-homogeneous tournaments with even number of vertices.
Spatial Distance-based Uniformity Evaluation Method for Power Flow Datasets

: Research on the application of artificial intelligence (AI) methods in power grid analysis has been widely conducted. The power flow dataset required for the training of AI models is not uniformly distributed, and to improve the quality of the dataset, related research has generally applied tuning methods that tend to have a more uniform sample distribution, resulting in improved performance of AI models. However, the lack of research on methods to evaluate the uniformity of distribution of the dataset poses an obstacle to the judgment of the validity of uniformity adjustment and the analysis of the impact of uniformly distributed datasets on the performance of AI models. This paper proposes a method to evaluate the uniformity of power flow datasets by using the calculation of distances in the high-dimensional feature space of the flow datasets and plotting the minimum distance statistics as a way to evaluate the uniformity of the flow datasets. It was also tested on the power flow dataset of 36-node grid and evaluated separately for two uniformity levels, which can clearly represent the uniformity of distribution of the dataset in the high-dimensional feature space.
Finite mixture models with predictive recursion marginal likelihood

Estimation of nite mixture models when the mixing distribution support is unknown is an important and challenging problem. In this paper, a new approach is given based on the recently proposed predictive recursion marginal likelihood (PRML) method. By taking a suciently ne grid as a set of candidate support points, one may treat the support itself as an unknown parameter to be estimated. The PRML approach asymptotically integrates out the mixing distribution itself, leaving an approximate marginal likelihood for the support, which can be used for estimation. We employ a computationally ecient version of simulated annealing for the large-scale combinatorial optimization problem. Theory is given which shows that the PRML estimate will asymptotically identify the best mixture model supported on a subset of the candidate grid, where \best" is measured with respect to the Kullback{Leibler divergence on the mixture scale. Real and simulated data examples show that the PRML method compares favorably to existing Bayesian and non-Bayesian methods in terms of mixture density estimation accuracy and model parsimony.
ASY-SONATA: Achieving Geometric Convergence for Distributed Asynchronous Optimization

Can one obtain a geometrically convergent algorithm for distributed asynchronous multi-agent optimization? This paper provides a positive answer to this open question. The proposed algorithm solves multi-agent (convex and nonconvex) optimization over static digraphs and it is asynchronous, in the following sense: i) agents can update their local variables as well as communicate with their neighbors at any time, without any form of coordination; and ii) they can perform their local computations using (possibly) delayed, out-of-sync information from the other agents. Delays need not obey any specific profile, and can also be time-varying (but bounded). The algorithm builds on a tracking mechanism that is robust against asynchrony (in the above sense), whose goal is to estimate locally the average of agents' gradients. When applied to strongly convex functions, we prove that it converges at an R-linear (geometric) rate as long as the step-size is sufficiently small. A sublinear convergence rate is proved, when nonconvex problems and/or diminishing, uncoordinated step-sizes are considered. Preliminary numerical results demonstrate the efficacy of the proposed algorithm and validate our theoretical findings.
On the second derivatives of periods and braking indices in radio pulsars

The analysis of some braking mechanisms for neutron stars was carried out to determine the sign of the second derivative of the pulsar period. This quantity is the important parameter for calculations of the braking index n. It is shown that this derivative can be positive and lead to decreasing of n. It is necessary to correct the methods of calculations of n used this moment because they are based as a rule on the suggestion on the constancy of pulsar parameters (magnetic fields, angles between some axes and so on). The estimations of corrections to braking indices are obtained. It is shown that these corrections can be marked for pulsars with long periods and their small derivatives.
On Dynamic Cumulative Residual Entropy of Order Statistics

The entropy functions are useful tools to measure the uncert ainty in a random variable. Dynamic Cumulative Residual Entropy (DCRE) introduced by Asadi and Zohrevand [ 1] as a useful dynamic measure of Cumulative Residual Entropy . They studied some properties and applications of these measures. In this paper, Dynamic Cumulative Residual Entropy is proposed bas ed on order statistics and under conditions is showed a characterizati on result that Dynamic Cumulative Residual Entropy of order statistics can determine the distribution function uniquely. Then some pr operties for DCRE of order statistics is presented.
Native Execution of GraphQL Queries over RDF Graphs Using Multi-Way Joins

Purpose: The query language GraphQL has gained significant traction in recent years. In particular, it has recently gained the attention of the semantic web and graph database communities and is now often used as a means to query knowledge graphs. Most of the storage solutions that support GraphQL rely on a translation layer to map the said language to another query language that they support natively, for example SPARQL. Methodology: Our main innovation is a multi- way left-join algorithm inspired by worst-case optimal multi-way join algorithms. This novel algorithm enables the native execution of GraphQL queries over RDF knowledge graphs. We evaluate our approach in two settings using the LinGBM benchmark generator. Findings: The experimental results suggest that our solution outperforms the state-of-the-art graph storage solution for GraphQL with respect to both query runtimes and scalability. Value: Our solution is implemented in an open- sourced triple store, and is intended to advance the development of representation-agnostic storage solutions for knowledge graphs.
Diagnostic Segmentation Based on Kidney Medical Image

Lesion segmentation of medical images is an important component of smart medicine. The development of deep learning technology is followed by rapid advancement in lesion segmentation technology of medical images. Though the present segmentation technology can retain spatial features, insufficient spatial features are retained with low segmentation accuracy. Our proposed PST-UNet model combines transformer with U-shaped structure and better infuses encoder's multi-scale features by using convolution fusion module. PST-UNet model adopts two types of block Swin transform at encoder and decoder ends respectively. Renal lesion data tends to present a normal distribution. Therefore, to preserve more spatial features and enhance the precision of renal lesion segmentation, Swin transformer block and full GELU (Gaussian Error Linear Unit) activation function are introduced at the encoder end. Similarly, at the decoder end, Swin transformer block, full GELU activation function, up-sampling and jumper wires from the convolution fusion module are also introduced.
Tribological Properties of PLA 3D Printed at Different Extrusion Temperature

Fused deposition modelling (FDM) 3D printing is widely used to manufacture prototype. To manufacture functional products with FDM 3D printing, several existing challenges have to be solved. Tribological behaviour of 3D printed parts has to be improved and optimised. In current study, friction and wear behaviours of 3D printed polylactic acid (PLA) printed at different extrusion temperature (190°C, 200°C, 210°C, 220°C, 230°C) sliding against steel disc were investigated. Pin on disc experiments that complies with ASTM G99-95a (2000) were conducted at a normal load of 15 N, and rotational speed of 150 RPM (0.69 m/s). Results showed that increasing the extrusion temperature resulted in a lower pin wear (specific mass loss) and higher friction coefficients. Pins printed at 190°C showed to have the biggest pin mass loss and lowest friction coefficients, whereas pins printed at 230°C showed to have the lowest pin mass loss and highest friction coefficients. This indicates the higher the extrusion temperature, the more capable it is for the deposited material to flow and homogenise with the neighbouring material which creates a flatter surface with less void between layers. Thus, effectively improving the interlayer bond making the FDM 3D printed part less susceptible to shear stress and delamination.
Search for Young and Intermediate-age Stellar Populations in the Galactic Center with Subaru/MOIRCS and CO Narrowband Filters

Extinction toward the Galactic Center (GC) is extreme and limits observations of its stars to the infrared. In addition, the extinction varies on scales of arcseconds. It is therefore highly challenging to distinguish between different stars photometrically and to identify young, massive, or intermediate-age stars that can trace recent star formation. Here we report the results of near-infrared imaging observations of a 6. ′0×3. ′3 region at the GC that encompasses the northwest part of the nuclear star cluster and parts of the nuclear stellar disk. We carried out the observations with the wide-field camera MOIRCS of the Subaru Telescope, using three narrowband (NB) filters. Two of the filters trace the stellar continuum, while we use the third one to measure the strength of the CO absorption features that are prominent in the spectra of cool giants, shallower in the spectra of warmer stars, and absent in young massive stars. Thus, we aim to distinguish between old late-type giants and young and intermediate-age stars. We have found 131 candidates of young and intermediate-age stars. The magnitude limit is ≈11.25 in the extinction-corrected K-band magnitude. The contamination of the candidates by old giants is estimated to be 26%. We show that our method allows us to identify correctly four out of five spectroscopically confirmed massive stars, and 68 of 75 spectroscopically confirmed cool stars (91%). Our results suggest that imaging observations with a CO NB filter can be complementary to other methods to identify massive stars in the GC.
Secondary School Gifted Students’ attitude toward the Classera Learning Management System in Saudi Arabia

SECONDARY SCHOOL GIFTED STUDENTS’ ATTITUDE TOWARD THE CLASSERA LEARNING MANAGEMENT SYSTEM IN SAUDI ARABIA Mansour Alamri, Ph.D. Department of Educational Technology, Research and Assessment Northern Illinois University, 2022 Pi-Sui Hsu, Director The present study examined secondary students’ attitude toward the Classera learning management system (LMS) within the context of gifted education in Saudi Arabia. The theoretical framework that guided this study was the technology acceptance model (TAM). Data were collected using the TAM survey, which consisted three subscales: perceived usefulness, perceived ease of use, and attitude. The data were collected from 306 gifted students in a secondary school in Saudi Arabia. Results indicated that gifted students’ attitude toward the Classera LMS is positive and showed that perceived usefulness and perceived ease of use were statistically significant predictors of gifted students’ attitude toward Classera LMS, while qualitative data results highlighted several benefits of using the Classera LMS among gifted students and demonstrated several challenges. The results showed that Classera LMS is an easy tool to use and useful for learning among gifted students. This study suggests constant development of the Classera LMS. Yet, Classera appears ready to be used in gifted students’ schools at the secondary level. NORTHERN ILLINOIS UNIVERSITY DEKALB, ILLINOIS
Low and Borderline Ankle Brachial Index is Associated with Intracranial Aneurysms -- a Retrospective Cohort Study

Background: A low ankle-brachial index (ABI) is associated with a higher risk of cardiovascular diseases, stroke, and systemic inflammation. Intracranial aneurysms (IAs) share similar risk factors with other cardiovascular diseases. However, there is a lack of studies that investigate the association between low ABI and IAs. Methods: We conducted a retrospective review of all patients (n=2751) who had ABI measurements at our tertiary hospital between 1 January 2011 to 31 December 2013. Patients with available cerebrovascular imaging or a diagnosis of ruptured IA were included in the study (n=776) to examine the association between ABI and saccular IAs. The patients were classified into four groups as follows: low ABI (? 0.9, n=464), borderline ABI (0.91-0.99; n=47), high ABI (>1.4, n=57), and normal ABI (1.00-1.40; n=208). Results: The prevalence of IAs was 20.3% (18.1% unruptured IAs) in the low ABI group, 14.9% (12.8% unruptured IAs) in the borderline ABI group, 7.0 % (5.3% unruptured IAs) in the high ABI group, and 2.4% (1.9% unruptured IAs) in the normal ABI group (p<0.001). Sex- and age-adjusted multinomial regression, including clinically relevant variables, revealed that low ABI (odds ratio [OR], 11.36; 95% confidence interval [CI], 4.01-31.60; p<0.001) and borderline ABI (0.91-0.99) (OR, 7.10; 95% CI, 1.90-26.52; p=0.003) were the only variables significantly associated with unruptured IAs. Conclusions: The prevalence of IAs was almost 9-fold higher in the low ABI group and nearly 7-fold higher in the borderline ABI group when compared to the normal ABI group. The findings suggest that ABI measurements can be clinically relevant for identifying individuals at higher risk of IAs and may help guide screening and preventive strategies.
The impact of spectral basis set composition on estimated levels of cingulate glutamate and its associations with different personality traits

1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate whether the basis set configuration -- which metabolites are included in the basis set used for analysis -- would affect the accuracy of the spectral fit and estimated glutamate concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits. To ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel. Our findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., glutamate in mol/kg, Glx (glutamate+glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results. Our study highlights that scientific results may be significantly altered depending on the choices of metabolites included the basis set, and with that the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results when using MR spectroscopy. Overall, it emphasizes the need for standardized analysis pipelines and reporting.
Design of 1060 nm tapered lasers with separate contacts

High brightness gain guided tapered lasers emitting at 1060 nm were designed, simulated and fabricated. The devices showed excellent beam quality up to 2 W cw, and the experimental results were similar to the predictions of the simulations. Tapered lasers with separate contacts were designed with the goal of achieving a high modulated power with a low modulating current.