Miracle-dz/UniFAR-FTUs huggingface dataset:
Tax Compliance Behavior Among Malaysian Taxpayers: A Dual-stage PLS-SEM and ANN Analysis

The tax system provides the necessary financial resources to a country’s administration to use the collected resources for the welfare of the general public and the development of general infrastructure. Therefore, compliance with the prevailing tax system is necessary for a country’s political and administrative system to survive. The current study aims to explore the intention to comply and the compliance behavior toward the tax system among Malaysian taxpayers. The data were collected using the survey method, and quantitative data were analyzed using the dual-stage methods of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN). The analysis results showed that the perceived fairness of the tax system, tax penalties, and tax awareness were significantly related to the intention to comply with the tax rules and regulations. Furthermore, the intention to comply with the tax system significantly influenced tax compliance behavior. The ANN analysis confirms that tax awareness, tax penalties, and fairness perception are the three most important factors influencing tax compliance. The tax authorities need to build the taxpayers’ confidence in the prevailing tax system and streamline the tax system to reduce tax complexity. Consequently, the taxpayers will start paying the correct and timely tax liabilities. The study’s limitations and future research avenues are documented at the end of this paper.
Preheating in an Asymptotically Safe Quantum Field Theory

We consider reheating in a class of asymptotically safe quantum field theories recently studied in \cite{Litim:2014uca, Litim:2015iea}. These theories allow for an inflationary phase in the very early universe. Inflation ends with a period of reheating. Since the models contain many scalar fields which are intrinsically coupled to the inflaton there is the possibility of parametric resonance instability in the production of these fields, and the danger that the induced curvature fluctuations will become too large. Here we show that the parametric instability indeed arises, and that hence the energy transfer from the inflaton condensate to fluctuating fields is rapid. Demanding that the curvature fluctuations induced by the parametrically amplified entropy modes do not exceed the upper observational bounds puts a lower bound on the number of fields which the model of Ref. ~\cite{Litim:2014uca, Litim:2015iea} must contain. This bound also depends on the total number of e-foldings of the inflationary phase.
Lower-Income Households and Ride-Hail

Problem, research strategy, and findings: New transportation options like ride-hail can expand accessibility without the costs of car ownership. Ride-hail’s potential is particularly salient for lower-income and zero-car households. We used interviews and a national (U.S.) survey to examine how and why lower-income travelers in the United States use ride-hail. Survey and interview responses provided a temporal snapshot and thus reflect, in part, travel challenges specific to COVID-19. Findings suggest that lower-income travelers, particularly those without personal cars, use ride-hail in ways distinct from those typically reported in broader travel surveys. Individuals without cars are more likely to use ride-hail, and use it more often, compared with people with cars, particularly to fill spatial and temporal gaps in public transit service and to access medical care and groceries. Costs and price unpredictability remain significant barriers limiting travelers’ use of ride-hail services. Takeaway for practice: This research demonstrates a latent need for car access among lower-income travelers. Substantial gaps in alternative modes pose challenges for travelers seeking reliable and timely transportation. Planners should invest in transit, biking, and walking to provide robust alternatives to car ownership. Such investments, however, take time. In the meantime, cities and agencies should consider subsidizing ride-hail trips to bridge existing gaps in the transportation network.
Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations

Recently, learning invariant predictors across varying environments has been shown to improve the generalization of supervised learning methods. This line of investigation holds great potential for application to biological problem settings, where data is often naturally heterogeneous. Biological samples often originate from different distributions, or environments. However, in biological contexts, the standard "invariant prediction" setting may not completely fit: the optimal predictor may in fact vary across biological environments. There also exists strong domain knowledge about the relationships between environments, such as the evolutionary history of a set of species, or the differentiation process of cell types. Most work on generic invariant predictors have not assumed the existence of structured relationships between environments. However, this prior knowledge about environments themselves has already been shown to improve prediction through a particular form of regularization applied when learning a set of predictors. In this work, we empirically evaluate whether a regularization strategy that exploits environment-based prior information can be used to learn representations that better disentangle causal factors that generate observed data. We find evidence that these methods do in fact improve the disentanglement of latent embeddings. We also show a setting where these methods can leverage phylogenetic information to estimate the number of latent causal features.
The degree of nonminimality is at most 2

It is shown that if p ∈ S ( A ) is a complete type of Lascar rank at least 2, in the theory of differentially closed fields of characteristic zero, then there exists a pair of realisations a 1 ,a 2 such that p has a nonalgebraic forking extension over Aa 1 a 2 . Moreover, if A is contained in the field of constants then p already has a nonalgebraic forking extension over Aa 1 . The results are also formulated in a more general setting.
Phosphatidylethanol in Maternal or Neonatal Blood to Detect Alcohol Exposure during Pregnancy: A Systematic Review

Background: Alcohol consumption during pregnancy, even at low doses, may damage the fetus. Pregnant women tend to underreport their alcohol consumption generating the need for sensitive and specific biomarkers, among which PEth has emerged due to its high specificity and possibility to be measured in both maternal and neonatal blood. The aim of this study is to systematically review the latest 20 years of literature for depicting the state of the art, the limitations, and the prospects of PEth for estimating alcohol consumption during pregnancy. Materials and methods: A systematic search, adhering to PRISMA guidelines, of the latest 20 years of literature through “MeSH” and “free-text” protocols in the databases PubMed, SCOPUS, and Web of Science, with time limits 1 January 2002–1 March 2022, was performed. The inclusion criteria were as follows: PEth used for detecting alcohol consumption during pregnancy, quantified in blood through liquid chromatography coupled to mass spectrometry, and full texts in the English language. Opinion papers, editorials, and narrative reviews were excluded. Results: Sixteen (16) papers were included in the present review (0.81% of total retrieved records). All the included records were original articles, of which there were seven prospective cohort/longitudinal studies, six cross-sectional studies, two observational-descriptive studies, and one retrospective study. All studies assayed PEth in at least one biological matrix; seven (7) studies quantified PEth in maternal blood, seven studies in newborn blood, and only two studies in both maternal and neonatal blood. In several included papers, PEth proved more sensitive than self-reports for identifying pregnant women with an active alcohol intake with the diagnostic efficiency improving with the increase of the maternal alcohol intake. Conclusions: Further studies, performed on wider and well-stratified populations, are needed to drive any definitive conclusion. PEth is a promising marker for monitoring alcohol use in pregnancy; however, at the present time, its use is still limited mainly by the absence of a globally agreed interpretative cut-off, the paucity of data regarding its specificity/sensitivity, and the lack of standardization on the diagnostic efficiency of the different isoforms.
On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping

The accelerated proximal methods (APM) have become one of the most important optimization tools for large-scale convex composite minimization problems, due to their wide range of applications and the optimal convergence rate in first-order algorithms. However, most existing theoretical results of APM are obtained by assuming that the gradient oracle is exact and the proximal mapping must be exactly solved, which may not hold in practice. This work presents a theoretical study of APM by allowing to use inexact gradient oracle and approximate proximal mapping. Specifically, we analyze inexact APM by improving the approximate duality gap technique (ADGT) which was originally designed for convergence analysis for first-order methods with both exact gradient oracle and proximal mapping. Our approach has several advantages: 1) we provide a unified convergence analysis that allows both inexact gradient oracle and approximate proximal mapping; 2) our proof is generic that naturally recovers the convergence rates of both accelerated and non-accelerated proximal methods, on top of which the advantages and the disadvantages of acceleration can be easily derived; 3) we derive the same convergence bound as previous methods in terms of inexact gradient oracle, but a tighter convergence bound in terms of approximate proximal mapping.
Verification of high‐speed solar wind stream forecasts using operational solar wind models

High‐speed solar wind streams emanating from coronal holes are frequently impinging on the Earth's magnetosphere causing recurrent, medium‐level geomagnetic storm activity. Modeling high‐speed solar wind streams is thus an essential element of successful space weather forecasting. Here we evaluate high‐speed stream forecasts made by the empirical solar wind forecast (ESWF) and the semiempirical Wang‐Sheeley‐Arge (WSA) model based on the in situ plasma measurements from the Advanced Composition Explorer (ACE) spacecraft for the years 2011 to 2014. While the ESWF makes use of an empirical relation between the coronal hole area observed in Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images and solar wind properties at the near‐Earth environment, the WSA model establishes a link between properties of the open magnetic field lines extending from the photosphere to the corona and the background solar wind conditions. We found that both solar wind models are capable of predicting the large‐scale features of the observed solar wind speed (root‐mean‐square error, RMSE ≈100 km/s) but tend to either overestimate (ESWF) or underestimate (WSA) the number of high‐speed solar wind streams (threat score, TS ≈ 0.37). The predicted high‐speed streams show typical uncertainties in the arrival time of about 1 day and uncertainties in the speed of about 100 km/s. General advantages and disadvantages of the investigated solar wind models are diagnosed and outlined.
Social Support and Self-Efﬁcacy on Turnover Intentions: The Mediating Role of Conﬂict and Commitment

: Turnover intentions are a phenomenon that affects the life of organizations and causes highly negative consequences. Based on previous studies, it is possible to consider antecedents to turnover in terms of both individual and social perceived resources, which previous research does not usually examine simultaneously. The aim of this study was to explore the role of both resources (individual and social) on turnover intentions. Thus, we hypothesized that perceived social support and self-efﬁcacy have an impact on turnover intentions and that this relationship is mediated by interpersonal conﬂict and Affective Commitment. A total of 392 Italian employees completed a self-report questionnaire. A structural equation model was tested. The results showed that interpersonal conﬂict and Affective Commitment fully mediated the relationship between social support,
Lexical Demands Across Text Genres — How Many Words Do Learners Need to Understand Different Types of Texts ?

The abstract is an integral part of a scientific paper. Despite the importance of abstracts, very little research has investigated the vocabulary size needed to read abstracts in scientific papers. This present study analyzed the lexical profile of 26 million words from approximately 100,000 scholarly abstracts across 10 major subjects of science. The results showed that the vocabulary size of the most frequent 7,000 and 15,000 word families in the British National Corpus/Corpus of Contemporary American English (BNC/COCA) word list plus proper nouns, marginal words, transparent compounds, acronyms were needed to gain 95% and 98% coverage of the abstract corpus, respectively. However, data from cross-disciplinary analyses demonstrated significant differences in the lexical demands between abstracts of different fields of study. The 570 word families in the Academic Word List were found to make up for 13.77% of the words in the corpus. Implications for the use of abstracts in language classrooms were discussed.
STAR FORMATION IN A QUASAR DISK

Using a version of the ZEUS code, we carry out two-dimensional simulations of self-gravitating shearing sheets, with application to QSO accretion disks at a few thousand Schwarzschild radii, corresponding to a few hundredths of a parsec for a 100-million-solar-mass black hole. Radiation pressure and optically thick radiative cooling are implemented via vertical averages. We determine dimensionless versions of the maximum surface density, accretion rate, and effective viscosity that can be sustained by density-wave turbulence without fragmentation. Where fragments do form, we study the final masses that result. The maximum Shakura–Sunyaev viscosity parameter is approximately 0.4. Fragmentation occurs when the cooling time is less than about twice the shearing time, as found by Gammie and others, but can also occur at very long cooling times in sheets that are strongly radiation-pressure dominated. For accretion at the Eddington rate onto a 108 solar-mass black hole, fragmentation occurs beyond 4000 Schwarzschild radii, rS. Near this radius, initial fragment masses are several hundred suns, consistent with estimates from linear stability; final masses after merging increase with the size of the sheet, reaching several thousand suns in our largest simulations. With increasing black hole mass at a fixed Eddington ratio, self-gravity prevails at smaller multiples of rS, where radiation pressure is more important and the cooling time is longer compared to the dynamical time; nevertheless, fragmentation can occur and produces larger initial fragment masses. Because the internal thermal and gravitational energies of these massive, radiation-pressure-dominated fragments nearly cancel, small errors in energy conservation can cause spurious results such as spontaneous dissolution of isolated bodies, unless special care is taken. This is likely to be a challenge for all Eulerian codes in self-gravitating regimes where radiation pressure dominates.
TRACING THE OUTFLOW OF A z = 0.334 FeLoBAL: NEW CONSTRAINTS FROM LOW-IONIZATION ABSORBERS IN FBQS J1151+3822

We show for the first time that FBQS J1151+3822 is an iron low-ionization broad absorption line quasar (FeLoBAL QSO), the second-brightest and second-closest known example of this class. He i* and Fe ii together act as an effective analytical tool, allowing us to obtain useful kinematic constraints from photoionization models of the outflow without needing to assume any particular acceleration model. The main outflow's log ionization parameter is −1.5, the log hydrogen density (cm−3) 5.5–8, the log hydrogen column density (cm−2) 21.7–21.9, the absorption radius 7.2–127 pc, and the kinetic luminosity is 0.16%–4.5% of the bolometric luminosity. We obtain line-of-sight covering fractions of ∼0.25 for strong Fe ii, ∼0.5 for He i*, and ∼0.6 for Mg ii. Narrower and shallower absorption lines from weaker Fe ii and Mn ii with an outflow velocity of ∼3400 km s−1 have appeared between 2005 and 2011, suggesting that dense cores may have condensed inside the main outflow. Consideration of the literature might suggest that the FBQS J1151+3822 outflow is a member of a rare and distinct subclass of FeLoBALs with high densities and correspondingly small absorption radii. We find, however, that such outflows are not necessarily a distinct subclass, and that their apparent rarity could be a symptom of selection bias in studies using density-sensitive lines.
The Multiple Systems in The Young Stellar Cluster IRAS 05137+3919

Four binary objects and one triplet have been revealed in the young stellar cluster located in the vicinity of IRAS 05137+3919 source on a distance 4.4 kpc with the use of statistic analysis. They are including the pair of AeBe stars. The percentage of the multiple systems in the cluster is mf = 5 and cp = 10.The mass of the multiple systems' components are located in the range from 1 to 8 Msol and log P (rotation period in years) - from 4.4 to 4.7. The median value of the mass ratio of the components is q = 0.86. The percentage of the multiple systems and their parameters in this cluster is resembling with the data obtained in the other star forming regions (ONC, Perseus, U Sco A), in which the value of mf and cp parameters are comparable with the results obtained for field's stellar population.
Gaussian Processes for Regression and Optimisation

Gaussian processes have proved to be useful and powerful constructs for the purposes of regression. The classical method proceeds by parameterising a covariance function, and then infers the parameters given the training data. In this thesis, the classical approach is augmented by interpreting Gaussian processes as the outputs of linear filters excited by white noise. This enables a straightforward definition of dependent Gaussian processes as the outputs of a multiple output linear filter excited by multiple noise sources. We show how dependent Gaussian processes defined in this way can also be used for the purposes of system identification. Onewell known problem with Gaussian process regression is that the computational complexity scales poorly with the amount of training data. We review one approximate solution that alleviates this problem, namely reduced rank Gaussian processes. We then show how the reduced rank approximation can be applied to allow for the efficient computation of dependent Gaussian processes. We then examine the application of Gaussian processes to the solution of other machine learning problems. To do so, we review methods for the parameterisation of full covariance matrices. Furthermore, we discuss how improvements can be made by marginalising over alternative models, and introduce methods to perform these computations efficiently. In particular, we introduce sequential annealed importance sampling as a method for calculating model evidence in an on-line fashion as new data arrives. Gaussian process regression can also be applied to optimisation. An algorithm is described that uses model comparison between multiple models to find the optimum of a function while taking as few samples as possible. This algorithm shows impressive performance on the standard control problem of double pole balancing. Finally, we describe how Gaussian processes can be used to efficiently estimate gradients of noisy functions, and numerically estimate integrals.
Ultraviolet spectroscopy of the blue supergiant SBW1: the remarkably weak wind of a SN 1987A analogue

National Aeronautics and Space Administration (NASA) through HST grants from the Space Telescope Science Institute [GO-12924, GO-11637, GO-11977, GO-13390, GO-13791]; NASA [NAS5-26555]; NSF [AST-1210599, AST-1515559]; Ambizione Fellowship of the Swiss National Science Foundation
TDLM - A Transonic Doublet Lattice Method for 3-D Potential Unsteady Transonic Flow Calculation and its Application to Transonic Flutter Prediction.

In this paper, a Transonic Doublet Lattice Method (TDLM) for calculating unsteady transonic pressure distributions and airloads is developed. The flow around harmonically surfaces is described by superposition of a steady mean transonic flow and an unsteady harmonic flow. The unsteady flow component is modelled by acceleration doublets on the mean wing surface and a source distribution in the flow field near the wing. The time-linearized unsteady transonic small perturbation equation is solved by an integral equation method. The unsteady aerodynamic results for a rectangular wing, the Northrop F5 wing, and the LANN wing in pitching oscillation are presented. Comparison is made with experimental and other numerical results. Agreement with the experimental results is very good. The flutter boundary of the semi-span AMP flutter model is calculated. The results show that the flutter boundary compares well with the test results. A parametric study of the influence of panel numbers in chordwise and spanwise directions is made, showing a very good convergence. The computing time for a six-mode flutter calculation per each reduced frequency is about 10 minutes on an IBM 3090. All results show that the TDLM is an accurate and economic unsteady transonic aerodynamic tool for routine transonic flutter calculation.
THE FOURTH IBIS/ISGRI SOFT GAMMA-RAY SURVEY CATALOG

In this paper, we report on the fourth soft gamma-ray source catalog obtained with the IBIS gamma-ray imager on board the INTEGRAL satellite. The scientific data set is based on more than 70 Ms of high-quality observations performed during the first five and a half years of the Core Program and public observations. Compared to previous IBIS surveys, this catalog includes a substantially increased coverage of extragalactic fields, and comprises more than 700 high-energy sources detected in the energy range 17–100 keV, including both transients and faint persistent objects that can only be revealed with longer exposure times. A comparison is provided with the latest Swift/BAT survey results.
The CoRoT star 105288363: Strong cycle-to-cycle changes of the Blazhko modulation

We present an analysis of the COnvection, ROtation and planetary Transits (CoRoT) star 105288363, a new Blazhko RR Lyrae star of type RRab (f 0 = 1.7623 d ―1 ), observed with the CoRoT spacecraft during the second long run in the direction of the Galactic Centre (LRc02, time-base 145 d). The CoRoT data are characterized by an excellent time-sampling and a low noise amplitude of 0.07 mmag in the 2-12 d ―1 range and allow us to study not only the fine details of the variability of the star, but also long-term changes in the pulsation behaviour and the stability of the Blazhko cycle. We use, among other methods, standard Fourier analysis techniques and O―C diagrams to investigate the pulsational behaviour of the Blazhko star 105288363. In addition to the frequency pattern expected for a Blazhko RR Lyrae star, we find an independent mode (f 1 = 2.984 d ―1 ) showing a f 0 /f 1 ratio of 0.59 which is similar to that observed in other Blazhko RR Lyrae stars. The bump and hump phenomena are also analysed, with their variations over the Blazhko cycle. We carefully investigated the strong cycle-to-cycle changes in the Blazhko modulation (P B = 35.6 d), which seem to happen independently and partly diametrically in the amplitude and phase modulations. Furthermore, the phasing between the two types of modulations is found to change during the course of the observations.