Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Ancestry Estimates within Dutch Family Units and Across Genotyping Arrays: Insights from Empirical Analysis Using Two Estimation Methods

Accurate inference of genetic ancestry is crucial for population-based association studies, accounting for population heterogeneity and structure. This study analyzes genome-wide SNP data from the Netherlands Twin Register to compare genetic ancestry estimates. The focus is on the comparison of ancestry estimates between family members and individuals genotyped on multiple arrays (Affymetrix 6.0, Affymetrix Axiom, and Illumina GSA). Two conventional methods, principal component analysis and ADMIXTURE, were implemented to estimate ancestry, each serving its specific purpose, rather than for direct comparison. The results reveal that as the degree of genetic relatedness decreases, the Euclidean distances of genetic ancestry estimates between family members significantly increase (empirical p < 0.001), regardless of the estimation method and genotyping array. Ancestry estimates among individuals genotyped on multiple arrays also show statistically significant differences (empirical p < 0.001). Additionally, this study investigates the relationship between the ancestry estimates of non-identical twin offspring with ancestrally diverse parents and those with ancestrally similar parents. The results indicate a statistically significant weak correlation between the variation in ancestry estimates among offspring and differences in ancestry estimates among parents (Spearman’s rho: 0.07, p = 0.005). This study highlights the utility of current methods in inferring genetic ancestry, emphasizing the importance of reference population composition in determining ancestry estimates.
Long‐term Changes in Soil Carbon and Nitrogen Fractions in Switchgrass, Native Grasses, and No‐till Corn Bioenergy Production Systems

Cellulosic bioenergy is a primary land-based climate mitigation strategy, with soil carbon (C) storage and nitrogen (N) conservation as important mitigation elements. Here, we present 13 years of soil C and N change under three cellulosic cropping systems: monoculture switchgrass ( Panicum virgatum L.), a five native grasses poly-culture, and no-till corn ( Zea mays L.). Soil C and N fractions were measured four times over 12 years. Bulk soil C in the 0–25 cm depth at the end of the study period ranged from 28.4 ( ± 1.4 se) Mg C ha − 1 in no-till corn, to 30.8 ( ± 1.4) Mg C ha − 1
Effective Chorin–Temam algebraic splitting schemes for the steady Navier–stokes equations

This paper continues some recent work on the numerical solution of the steady incompressible Navier–Stokes equations. We present a new method, similar to the one presented in Rebholz et al., but with superior convergence and numerical properties. The method is efficient as it allows one to solve the same symmetric positive‐definite system for the pressure at each iteration, allowing for the simple preconditioning and the reuse of preconditioners. We also demonstrate how one can replace the Schur complement system with a diagonal matrix inversion while maintaining accuracy and convergence, at a small fraction of the numerical cost. Convergence is analyzed for Newton and Picard‐type algorithms, as well as for the Schur complement approximation.
Radix-2k MSC FFT Architectures

In recent years, the SC FFT architecture has become popular for processing serial data. It requires a small number of components and achieves full utilization of the butterflies, which improves previous serial FFT architectures. By contrast, the MSC FFT architecture, which is the parallel version of the SC FFT, has not been studied in depth in the literature and it has not been analyzed if this new type of FFT architecture improves previous parallel FFTs. The aim of this paper is to provide a rigorous study of MSC architectures that expands the field of FFT architectures by incorporating fundamental knowledge about this promising FFT. With this goal, this paper proposes new MSC FFT architectures for any FFT size, radix, and parallelization. In order to derive these architectures, efficient modules have been developed. These modules are connected by permutation circuits to create the architectures. The optimization of the modules results in a reduction in the number of rotators and their complexity compared to previous designs. As a result, the proposed architectures not only achieve high throughput due to their parallel nature but also the lowest hardware complexity among parallel pipelined FFT architectures so far. To verify the architectures and compare the proposed approach to previous works, a 1024-point MSC FFT architecture has been implemented. Experimental results show that the architecture achieves a throughput of 1.32 gigasamples per second, and reduces the area and power consumption significantly with respect to previous designs.
Build, Borrow, Buy... or Bail?

The relationship between divestitures and acquisitions is generally presented in three ways: the focal firm makes divestitures to free up resources for future acquisitions; the focal firm divests redundant parts of a target firm they have acquired; or the focal firm may divest an acquired business entirely if the performance of the combined firm does not meet expectations. We theorize an additional relationship — if a bidding firm attempts to acquire a target firm but the transaction fails to close, we predict conditions under which the firm may pivot to divest resources related to the target firm rather than return to the status quo or attempt another mode of growth. To test this relationship, we augment previous methodological approaches with a novel method: matching successful and unsuccessful acquisition bids using the perceived risk of deal failure by using arbitrage spreads between the announced and spot price of the target. Consistent with this argument, we find that bidding firms make more divestitures in sectors related to the target after a failed bid.
Indonesian EFL Pre-Service Teachers’ Challenges and Solutions in Distance Learning Context

Since the Covid-19 phenomenon until the present, distance learning has become a common learning method. This learning method makes participation in the learning activity easier and more superficial for students, as the activity can be completed anywhere via online platforms and devices such as laptops and smartphones. However, many students still face obstacles when learning through this mode, which necessitates the development of solutions. While research on the challenges and solutions of distance learning from the perspective of general students has been conducted, little has been conducted from the perspective of Indonesian EFL pre-service teachers. This study, thus aimed to examine the distance learning challenges and solutions encountered by 10 Indonesian EFL pre-service teachers who were invited to be the participants of this study through a purposive sampling technique. The results show that the EFL pre-service teachers face four major challenges in distance learning, namely: network, platform, instruction and interaction, and boredom. As they must engage in distance learning activities, they must implement solutions to the associated difficulties. The solutions are finding a location with a good and stable internet connection, utilizing Wi-Fi, and preparing a backup of internet data, self-teaching or consulting with peers to master the application's functionality, asking the instructors in groups or individually, and being self-motivated. This study suggests that the government, educators, and parents must assist them.
Coronal Jets, and the Jet-CME Connection

Solar coronal jets have been observed in detail since the early 1990s. While it is clear that these jets are magnetically driven, the details of the driving process has recently been updated. Previously it was suspected that the jets were a consequence of magnetic flux emergence interacting with ambient coronal field. New evidence however indicates that often the direct driver of the jets is erupting field, often carrying cool material (a “minifilament”), that undergoes interchange magnetic reconnection with preexisting field ([1]). More recent work indicates that the trigger for eruption of the minifilament is frequently cancelation of photospheric magnetic fields at the base of the minifilament. These erupting minifilaments are analogous to the better-known larger-scale filament eruptions that produce solar flares and, frequently, coronal mass ejections (CMEs). A subset of coronal jets drive narrow “white-light jets,” which are very narrow CME-like features, and apparently a few jets can drive wider, although relatively weak, “streamer-puff” CMEs. Here we summarize these recent findings.
Phase asymmetry guided adaptive fractional-order total variation and diffusion for feature-preserving ultrasound despeckling

It is essential for ultrasound despeckling to remove speckle noise while simultaneously preserving edge features for accurate diagnosis and analysis in many applications. To preserve real edges such as ramp edges and low contrast edges, we first detect edges using a phase-based measure called phase asymmetry (PAS), which can distinguish small differences in transition border regions and varies from $0$ to $1$, taking $0$ in ideal smooth regions and taking $1$ at ideal step edges. We further propose three strategies to properly preserve edges. First, in observing that fractional-order anisotropic diffusion (FAD) filter has good performance in smooth regions while the fractional-order TV (FTV) filter performs better at edges, we leverage the PAS metric to keep a balance between FAD filter and FTV filter for achieving the best performance of preserving ramp edges. Second, considering that the FAD filter fails to protect low contrast edges by solely integrating gradient information into the diffusion coefficient, we integrate the PAS metric into the diffusion coefficient to properly preserve low contrast edges. Finally, different from fixed fractional order diffusion filters neglecting the differences between smooth regions and transition border regions, an adaptive fractional order is implemented based on the PAS metric to enhance edges. The experimental results show that our method outperforms other state-of-the-art ultrasound despeckling filters in both speckle reduction and feature preservation.
Higher-Order Stereophony

This work introduces a new theory for spatial audio recording and reproduction named Higher Order Stereophony. Through the use of the Taylor expansion, the technique accurately reproduces a sound field across a line that is orientated as the interaural axis of a listener, to attempt to recreate a set of desired binaural signals. The technique utilises loudspeaker amplitude panning, and is shown to encompass in its framework traditional Stereophony approaches such as the stereo sine law. Therefore, the technique expands Stereophony to higher orders and more loudspeakers, leading to a greater frequency range of accurate reproduction, in a similar manner to Higher Order Ambisonics. Higher Order Stereophony is shown to exhibit many similarities to Higher Order Ambisonics, and decoders to transition between the different sound field representations are derived. Higher Order Stereophony is also re-derived through a mode matching approach using a subset of spherical harmonics, those with degree index equal to zero only. The theoretical results are then validated through experimental measurements using various microphone arrays, considering the reproduced sound field across a single line and the reproduced spherical harmonic coefficients of the sound field.
A new generation of detectors for scanning x-ray beam imaging systems

Scanning x-ray beam imaging systems were first developed by American Science and Engineering, Inc. (AS&E) in the early 1970s [1]. Since then, these systems have found a wide range of applications in security inspection and non-destructive testing. Large-area detectors are most frequently used to collect backscattered radiation but smaller transmission detectors are also employed for selected applications. Until recently, only two basic detector designs have been used: large scintillator blocks with attached photomultiplier tubes (PMTs) or large-volume light-sealed boxes, lined with scintillating screens and port windows for PMTs. In both cases, the detectors have required considerable depth to provide acceptable light collection efficiency. A new design recently developed by AS&E relies on wavelength shifting fibres (WSF) for light collection. For the first time, this approach enables the construction of thin large-area detectors. Stacking layers of WSF ribbons and scintillating screens in varying combinations enables optimization of the detection efficiency for different applications. Taking separate readings from different layers provides an energy-sensitive signal combination. Energy sensitivity can be improved further by adding filtration between the signal channels. Several prototype configurations have been built and characterized for both backscatter and transmission imaging. A WSF-based detector has been commercialized for a transmission x-ray imaging application.
ZOOpt/ZOOjl: Toolbox for Derivative-Free Optimization

Recent advances of derivative-free optimization allow efficient approximating the global optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt/ZOOjl toolbox that provides efficient derivative-free solvers and are designed easy to use. ZOOpt provides a Python package for single-thread optimization, and ZOOjl provides a distributed version with the help of the Julia language for Python described functions. ZOOpt/ZOOjl toolbox particularly focuses on optimization problems in machine learning, addressing high-dimensional, noisy, and large-scale problems. The toolbox is being maintained toward ready-to-use tools in real-world machine learning tasks.
When acquisition and aphasia converge: the case of copula omission

This paper reports evidence for a convergence between child language acquisition and Broca’s aphasia in the domain of copula omission. Our data shows that, in the spontaneous speech of people with Broca’s aphasia (PWBA), copula omission is confined to aspectual predicates, replicating a finding previously reported by Becker (2002) for child English. This grammatical property is a much stronger predictor of copula omission than alternative, extra-grammatical factors, such as predicate length or utterance length. We argue that grammatical accounts which predict the fragility of Tense by virtue of its cartographic location, in terms of ‘tree-pruning’/‘growing trees’, fare better than others in explaining similarities in patterns of omission in these two populations.
Predicting turbidity in water distribution trunk mains using nonlinear autoregressive exogenous artificial neural networks

A nonlinear autoregressive exogenous artificial neural network model was developed to predict turbidity response in two different trunk mains with measured flow and turbidity data. Models were initially established to prepare the data and automatically select the appropriate events for model training. Then, an autoregressive exogenous network model was developed and applied to predict turbidity responses based on past events in the time series. A per site continual data driven calculation of turbidity event risk was included as an additional input to capture the effect of temporal distance between the selected events as well as increasing the accuracy of the predictions. The calculated normalised mean square error and mean absolute error showed that the developed model combined with the data preparation and pre- processing models provides good regressions on a future event with a period of 7 to 10 hours for a multi-step ahead prediction. Furthermore, the result of the autoregressive exogenous network was compared with the output of a feed-forward network where the former significantly outperformed the latter (R value of approximately 0.97 compared to 0.66).
Acculturative Stress and Quality of Life among Older Korean Immigrants: Do Religiosity and Resilience Matter?

The impact of acculturative stress on the quality of life among older adults has been widely acknowledged in previous literature. However, few studies have explored the role of religiosity and resilience in this relationship among older Korean immigrants. This dissertation explores the relationships between acculturative stress, religiosity, resilience, and quality of life among 300 older adults aged 65 years and over who live in an ethnic Korean enclave of Queens, New York. The study employs path analysis and multiple regression analyses to test hypothesized relationships among these variables. The key findings of the study are as follows: (1) higher acculturative stress was associated with lower quality of life after controlling for sociodemographic variables; (2) religiosity did not mediate the relationship between acculturative stress and quality of life; (3) resilience partially mediated the relationship between acculturative stress and quality of life; and (4) religiosity and resilience did not sequentially mediate the relationship between acculturative stress and quality of life. These findings highlight the importance of resilience in mediating the negative impact of acculturative stress on the quality of life among older Korean immigrants. Implications for social work practice, education, and future research are discussed, emphasizing the development of targeted interventions to address acculturative stress and enhance resilience among older Korean immigrants and exploring the potential roles of religiosity and other coping resources in this population. Additionally, the study underscores the need for ongoing professional development to improve cultural competence and sensitivity when working with older Korean immigrants.
Facial Attribute Prediction

In this thesis we propose a method for learning single CNN model performing multiple prediction tasks simultaneously. We formulate learning of the model parameters in the Maximum-Likelihood framework which allows to learn from both fully annotated and partially annotated examples. The proposed method is evaluated on the problem of prediction of attributes from an image of human face. We experimentally show that the proposed multitask prediction model has the same performance as an ensemble of independent CNN models each trained to perform a single prediction task. The proposed multitask model achieves super-human performance and it is comparable to the current state-of-the-art methods. In addition we verified that the proposed method can learn from multiple datasets with heterogeneous annotation of the attributes being the scenario often encountered in practice.
Average Method of Mixed Sampling Distribution Bayesian Model

Based on the study of Bayesian model average (BMA), this paper proposes to mix the prior distribution and sampling distribution to obtain the average method of the mixed sampling distribution Bayesian model overcoming the problem that traditional econometric modeling method does not explicitly consider the uncertainty of the model. If all the alternative models have the same parametric form, then the new Bayesian estimation will degenerate into the BMA estimator. The empirical results show that the method is better than Bayesian model average.
An organizational approach for discipline prediction in research projects

The prediction of research disciplines has gained increasing attention in recent years due to its potential implementations in a variety of fields, such as academic advising, career counseling, and academic research funding allocation. Research information systems storing projects (meta) data play a crucial role in managing and evaluating research (meta) data across different disciplines and fields of study. In this context, research projects are manually assigned one or more research disciplines to facilitate this process. This is usually done by research administrators due to the limited time the principal researchers themselves might have. In addition to being rather subjective and time-consuming, this can lead to inconsistencies in discipline assignments and hence impact the quality of data used for monitoring and reporting. To address these limitations various approaches have been proposed, in the literature, to predict disciplines associated with research documents, e.g., publications, and projects. The frequently used methods in bibliometrics are bibliographic coupling, co-citation, and direct citation [1]. These approaches used citation network analysis techniques to determine the disciplines related to a publication. More recently, machine learning techniques have been applied to classify research documents [2]–[4]. In these approaches, the publications’ abstracts were used as features to predict related disciplines. Machine learning techniques have been demonstrated to perform better than traditional approaches in bibliometrics. Although these approaches are useful, they may not be applicable to research information systems that lack citation data or have low-quality abstracts. In this paper, we propose a novel approach to predict the disciplines of research projects in a research information system. The proposed approach uses machine learning algorithms and extracted disciplines from researchers and their related information such as organizations, projects, co-authors on projects, publications, and co-authors on publications. By analyzing the disciplines from these resources, the proposed model can predict each project’s most appropriate research disciplines, providing a more objective and consistent approach to discipline assignment. This approach is helpful when there are no citation data or high-quality abstracts available. In the following sections, we describe the development and evaluation of our model, including the data sources and methods used to train the machine learning algorithms, as well as the performance metrics used to evaluate the accuracy and effectiveness of the proposed approach.
Does the Degree of Digitalization Inhibit Credit Risk Contagion of New Economy Firms

Based on 19 new economy firms in the Chengdu-Chongqing Economic Circle, this paper constructs a three-year new economy firms credit risk contagion network model from 2019 to 2021. On this basis, the topological properties of the network are measured from the full network structure and node attributes, and the contagion path of credit risk of new economic firms and the process of dynamic changes over time are analyzed. Further, the text analysis method is used to measure the degree of digitalization of new economy firms, combined with the credit risk contagion network, to study the effect of digitalization degree on the credit risk contagion of new economy firms. The final empirical results show that the degree of digitization has an inhibitory effect on the credit risk contagion of new economy firms, and the contagion inhibitory effect on the important "bridge" node firms in the contagion network are more significant.
Complementary and Alternative Medicine Use Among Women With Breast Cancer: A Systematic Review

Ausanee Wanchai, MSN, RN, is a nursing instructor at Boromarajonani College of Nursing, Buddhachinaraj, in Phitsanuloke, Thailand, and a doctoral student in the Sinclair School of Nursing at the University of Missouri in Columbia; and Jane M. Armer, PhD, RN, FAAN, is a professor and Bob R. Stewart, EdD, is emeritus of education and adjunct clinical professor, both in the Sinclair School of Nursing at the University of Missouri. (First submission December 2009. Revision submitted January 2010. Accepted for publication January 31, 2010.)