Miracle-dz/UniFAR-FTUs huggingface dataset:
On Directional Regression for Dimension Reduction

We introduce directional regression (DR) as a method for dimension reduction. Like contour regression, DR is derived from empirical directions, but achieves higher accuracy and requires substantially less computation. DR naturally synthesizes the dimension reduction estimators based on conditional moments, such as sliced inverse regression and sliced average variance estimation, and in doing so combines the advantages of these methods. Under mild conditions, it provides exhaustive and -consistent estimate of the dimension reduction space. We develop the asymptotic distribution of the DR estimator, and from that a sequential test procedure to determine the dimension of the central space. We compare the performance of DR with that of existing methods by simulation and find strong evidence of its advantage over a wide range of models. Finally, we apply DR to analyze a data set concerning the identification of hand-written digits.
Analysis of unsteady pipe flow using the modified finite element method

A modified finite element method is proposed to solve the unsteady pipe flow equations. This approach yields a six-point implicit scheme with two weighting parameters. An accuracy analysis carried out using the modified equation approach showed that the proposed scheme has higher accuracy compared to other methods. A comparison of experimental data and the results of numerical solution showed that the required damping and smoothing of a pressure wave can be obtained when numerical diffusion is produced by the applied method. It suggests that the physical dissipation process observed in the water hammer phenomenon is not represented properly in its mathematical model. Therefore, the classical system of water hammer equations seems to be incomplete. Copyright © 2004 John Wiley & Sons, Ltd.
Macroscale ecology in the 21st century: Toward multidisciplinary integration in advancement for scientific solutions of sustainable development

Ecology research has continuously expanded over the last several decades, and is recently experiencing rapid development at the macroscale. Both the number and scope of macro-ecological studies have increased significantly since the beginning of the 21st century. There is a need to reveal the recent hotspotsand challenges of macroscale ecology research, and the macro-ecology and macrosystems ecology were proposed as a way to do this, with global and regional sustainability recognized. Through scientometric analyses, we highlighted common research frontiers and challenges: (1) increased research on ecological functions and processes; (2) taking developmental issues as the core at the macroscale; and (3) continually refined data, scaling methods and models. It is time to solve these existing and possible future problems with multidisciplinary integration to materialize a strong base for ecological sciences to achieve goals of regional and global sustainable development.
Association of Insurance Type With Inpatient Surgical 30-Day Readmissions, Emergency Department Visits/Observation Stays, and Costs

Objective: To assess the association of Private, Medicare (MC), and Medicaid/Uninsured (MU) insurance type with 30-day emergency department visits/observation stays (EDOS), readmissions, and costs in a safety-net hospital (SNH) serving diverse socioeconomic status patients. Background: MC’s hospital readmission reduction program (HRRP) disproportionately penalizes SNHs. Methods: This retrospective cohort study used inpatient National Surgical Quality Improvement Program (2013–2019) data merged with cost data. Frailty, expanded operative stress score, case status, and insurance type were used to predict odds of EDOS and readmissions, as well as index hospitalization costs. Results: The cohort had 1477 Private; 1164 MC; and 3488 MU cases with a patient mean age 52.1 years [SD = 14.7] and 46.8% of the cases were performed on male patients. MU [adjusted odds ratio (aOR) = 2.69, 95% confidence interval (CI) = 2.38–3.05, P < 0.001] and MC (aOR = 1.32, 95% CI = 1.11–1.56, P = 0.001) had increased odds of urgent/emergent surgeries and complications versus Private patients. Despite having similar frailty distributions, MU compared to Private patients had higher odds of EDOS (aOR = 1.71, 95% CI = 1.39–2.11, P < 0.001), and readmissions (aOR = 1.35, 95% CI = 1.11–1.65, P = 0.004), after adjusting for frailty, OSS, and case status, whereas MC patients had similar odds of EDOS and readmissions versus Private. Hospitalization variable cost %change was increased for MC (12.5%) and MU (5.9%), but MU was similar to Private after adjusting for urgent/emergent cases. Conclusions: Increased rates and odds of urgent/emergent cases in MU patients drive increased odds of complications and index hospitalization costs versus Private. SNHs care for higher cost populations while receiving lower reimbursements and are further penalized by the unintended consequences of HRRP. Increasing access to care, especially for MU patients, could reduce urgent/emergent surgeries resulting in fewer complications, EDOS/readmissions, and costs.
Robust unsupervised classification with uncertain models

A classification procedure that estimates the parameters of an unsupervised vectored sample where the exact form of the underlying probability density of the samples is not known is developed. Following the estimation, the measurements are classified on the basis of maximized a-posteriori probability of classification. Since the Gaussianity assumption is at most an approximation to a realistic data set and gross errors are likely to appear, robustification and efficiency are emphasized. The data are only partially observed, and an a-priori model for the observed sample is not known, so that an uncertain modeling of the observations is desired. The missing data situation is handled by using the expectation maximization (EM) algorithm to estimate the unobserved quantities. The class of weighted M-estimators is defined and used in the robust classifier. An efficient simple numerical method for the solution of the weighted M-estimate for the large class of in contaminated distributions is presented. An extension to the case where samples of a particular class are known to be correlated is discussed, and a postprocessing step to re-estimate the parameters is proposed. <<ETX>>
Multiple velocity estimation in an image neighborhood

This paper presents a method for estimating the motion of moving objects using time-frequency signal analysis (TFSA). In particular, the peaks of the Wigner-Ville distribution (WVD) are used to solve multiple velocities in an image neighbourhood. This technique was tested and some results, in the noise free and noisy cases, are reported. This technique establishes a link between the phase-based approaches for motion estimation and the theory of time-frequency signal analysis. Established techniques of motion estimation and the fundamental concepts behind TFSA which are related to this approach are also presented. Some properties of time-frequency distributions are reported with a focus on the WVD and how it can be used to estimate instantaneous frequency.
Constant Regret Primal-Dual Policy for Multi-way Dynamic Matching

We study a discrete-time dynamic multi-way matching model. There are finitely many agent types that arrive stochastically and wait to be matched. State-of-the-art dynamic matching policies in the literature require the knowledge of all system parameters to determine an optimal basis of the fluid relaxation, and focus on controlling the number of waiting agents using only matches in the optimal basis [4,6,7]. In this paper, we propose a primal-dual policy that schedule matches for future arrivals based on an estimator for the dual solution. Our policy does not require the knowledge of optimal bases, and is the first to achieve constant regret at all times under unknown arrival rates. In addition, we show that when the arrival rates are known, the primal-dual policy achieves the optimal scaling as the lower-bound described in [6,7]. Furthermore, we find that when the arrival rates are known, the primal-dual policy can significantly outperform alternative dynamic matching policies in numerical simulations.
A homozygous KASH5 frameshift mutation causes diminished ovarian reserve, recurrent miscarriage, and non-obstructive azoospermia in humans

The meiosis-specific LINC complex, composed of the KASH5 and SUN1 proteins, tethers the moving chromosomes to the nuclear envelope to facilitate homolog pairing and is essential for gametogenesis. Here, we applied whole-exome sequencing for a consanguineous family with five siblings suffering from reproductive failure, and identified a homozygous frameshift mutation in KASH5 (c.1270_1273del, p.Arg424Thrfs*20). This mutation leads to the absence of KASH5 protein expression in testes and non-obstructive azoospermia (NOA) due to meiotic arrest before the pachytene stage in the affected brother. The four sisters displayed diminished ovarian reserve (DOR), with one sister never being pregnant but still having dominant follicle at 35 years old and three sisters suffering from at least 3 miscarriages occurring within the third month of gestation. The truncated KASH5 mutant protein, when expressed in cultured cells, displays a similar localization encircling the nucleus and a weakened interaction with SUN1, as compared with the full-length KASH5 proteins, which provides a potential explanation for the phenotypes in the affected females. This study reported sexual dimorphism for influence of the KASH5 mutation on human germ cell development, and extends the clinical manifestations associated with KASH5 mutations, providing genetic basis for the molecular diagnosis of NOA, DOR, and recurrent miscarriage.
Carbon-based nanoscience and nanotechnology: where are we, where are we heading?

'Without carbon, life cannot exist', the saying goes, and not only life. For technological development, carbon was the ultimate material of the 19th century. It allowed the beginnings of the industrial revolution, enabling the rise of the steel and chemical industries, it made the railways run, and it played a major role in the development of naval transportation. Silicon, another very interesting material which makes up a quarter of the earth's crust, became the material of the 20th century in its turn. It gave us the development of high performance electronics and photovoltaics with large fields of applications and played a pivotal role in the evolution of computer technology. The increased device performance of information and data processing systems is changing our lives on a daily basis, producing scientific innovations for a new industrial era. However, success breeds its own problems, and there is ever more data to be handled—which requires a nanoscience approach. This cluster aims to address various aspects, prospects and challenges in this area of great interest for all our futures. Carbon exists in various allotropic forms that are intensively investigated for their unusual and fascinating properties, from both fundamental and applied points of view. Among them, the sp2 (fullerenes, nanotubes and graphene) and sp3 (diamond) bonding configurations are of special interest since they have outstanding and, in some cases, unsurpassed properties compared to other materials. These properties include very high mechanical resistance, very high hardness, high resistance to radiation damage, high thermal conductivity, biocompatibility and superconductivity. Graphene, for example, possesses very uncommon electronic structure and a high carrier mobility, with charge carriers of zero mass moving at constant velocity, just like photons. All these characteristics have put carbon and carbon-related nanomaterials in the spotlight of science and technology research. The main challenges for future understanding include i) material growth, ii) fundamental properties, and iii) developing advanced applications. The reviews in this Cluster Issue of Journal of Physics D: Applied Physics cover carbon nanoparticles and nanotubes, graphene, nano-diamond and films. They address the most current aspects and issues related to their fundamental and outstanding properties, and describe various classes of high-tech applications based on these promising materials. Future prospects, difficulties and challenges are addressed. Important issues include growth, morphology, atomic and electronic structure, transport properties, superconductivity, doping, nanochemistry using hydrogen, chemical and bio-sensors, and bio-imaging, allowing readers to evaluate this very interesting topic and draw perspectives for the future.
LSFP-Tracker: An Autonomous Laser Stripe Feature Point Extraction Algorithm Based on Siamese Network for Robotic Welding Seam Tracking

Laser vision-based welding seam tracking has become the key technology to realize intelligent robotic welding, and extracting the feature point of welding seam is an essential step. Aiming at the low flexibility of classical methods, this article proposed a laser stripe feature point tracker (LSFP-tracker) based on siamese network for robotic welding seam tracking. First, a lightweight feature extraction network is constructed to produce high resolution feature maps that contain both low-level geometric details and high-level semantic information, which can ensure model's tracking accuracy and antinoise capability. Moreover, a feature refinement module is designed, which can enhance the tracking stability on specific seams through channel-wise emphasizing useful features. Subsequently, stripe's curvature and three preset templates were introduced to adaptively detect the initial feature point and corresponding bounding box, which is helpful to enlarge the applicability scope of LSFP-tracker. The experimental results show that the LSFP-tracker can maintain satisfying tracking performance on various welding seams, such as butt, V-groove, multi-pass seams, etc., even under the interference of welding noise. And the comparison tests prove the advantage of LSFP-tracker, which has great application potential in practical welding.
Novel WDR72 Mutations Causing Hypomaturation Amelogenesis Imperfecta

Amelogenesis imperfecta (AI) is a heterogeneous collection of hereditary enamel defects. The affected enamel can be classified as hypoplastic, hypomaturation, or hypocalcified in form. A better understanding of normal amelogenesis and improvements in our ability to diagnose AI through genetic testing can be realized through more complete knowledge of the genes and disease-causing variants that cause AI. In this study, mutational analysis was performed with whole exome sequencing (WES) to identify genetic etiology underlying the hypomaturation AI condition in affected families. Mutational analyses identified biallelic WDR72 mutations in four hypomaturation AI families. Novel mutations include a homozygous deletion and insertion mutation (NM_182758.4: c.2680_2699delinsACTATAGTT, p.(Ser894Thrfs*15)), compound heterozygous mutations (paternal c.2332dupA, p.(Met778Asnfs*4)) and (maternal c.1287_1289del, p.(Ile430del)) and a homozygous 3694 bp deletion that includes exon 14 (NG_017034.2:g.96472_100165del). A homozygous recurrent mutation variant (c.1467_1468delAT, p.(Val491Aspfs*8)) was also identified. Current ideas on WDR72 structure and function are discussed. These cases expand the mutational spectrum of WDR72 mutations causing hypomaturation AI and improve the possibility of genetic testing to accurately diagnose AI caused by WDR72 defects.
Electron microscopy study of the ferroelectric domains and domain wall structure in PbZr0.52Ti0.48O3

The crystallography of the domains and domain walls in lead zirconate titanate are studied using electron diffraciton and transmission electron microscopy. Both 90 ° and 180 ° domains are observed and the 90° domains are shown to be deformation twins with displacement along 〈110〉 on {110}. The thickness of the 90 ° domain wall is determined to be ≲100 A.
CONDITIONING VS. STANDARDIZATION FOR CONTRASTS ON ERRORS ATTRACTED TO STABLE LAWS

In multiple regression and other settings one encounters the problem of estimating sampling distributions for contrast operations applied to i.i. d. errors. Permutation bootstrap applied to least squares residuals has been proven to consistently estimate conditionalsampling distributions of contrasts, conditional upon order statistics of errors, even for long-tailed error distributions. How does this compare with the unconditional sampling distribution of the contrast when standardizing by the sample s.d. of the errors (or the residuals)? For errors belonging to the domain of attraction of a normal we present a limit theorem proving that these distributions are far closer to one another than they are to the limiting standard normal distribution. For errors attracted to α-stable laws with α ≤ 2 we construct random variables possessing these conditional and unconditional sampling distributions and develop a Poisson representation for their a.s. limit correlation ρα. We prove that ρ2= 1, ρα→ 1 for α → 0 + or 2 −, and ρα< 1 a.s. for α < 2.
How the home learning environment contributes to children’s social–emotional competence: A moderated mediation model

Introduction The home learning environment is the earliest contact learning environment in early childhood development, which plays an important role in the development of children’s social-emotional competence. However, previous studies have not clarified the precise mechanisms by which the home learning environment influences children’s social-emotional competence. Therefore, the purpose of the study is to explore the relationship between the home learning environment and its intrinsic structure (i.e. structural family characteristics, parental beliefs and interests, and the educational processes) and children’s social-emotional competence, and whether gender plays a moderating role in the relationship. Method The study randomly selected a sample of 443 children from 14 kindergartens in western China. The Home Learning Environment Questionnaire and the Chinese Inventory of Children’s Social-emotional competence scale were used to investigate the home learning environment and social-emotional competence of these children. Results (1) Structural family characteristics and parental beliefs and interests both had a significant positive predictive effect on children’s social-emotional competence. (2) The educational processes fully mediate between structural family characteristics, parental beliefs and interests, and children’s social-emotional competence. (3) Gender moderated the effect of the home learning environment on children’s social-emotional competence. Gender moderates not only the indirect effects between parental beliefs and interests and children’s social-emotional competence, but also the indirect effects between structural family characteristics and children’s social-emotional competence. At the same time, gender also moderated the direct effects between parental beliefs and interests and children’s social-emotional competence. Discussion The results emphasize the crucial role of the home learning environment in the development of children’s early social-emotional competence. Therefore, parents should pay attention to the home learning environment and improve their ability to create a home learning environment that promotes the positive development of children’s social-emotional competence.
Machine learning assisted interpretation of creep and fatigue life in titanium alloys

Making reliable predictions of the mechanical behavior of alloys with a prolonged service life is beneficial for many structural applications. In this work, we propose an interpretable machine learning (ML) approach to predict fatigue life cycles ( N f) and creep rupture life ( t r) in titanium-based alloys. Chemical compositions, experimental parameters, and alloy processing conditions are employed as descriptors for the development of gradient boost regression models for log-scaled N f and t r. The models are trained on an extensive experimental dataset, predicting log-scaled N f and t r with a very small root mean squared error of 0.17 and 0.15, respectively. An intuitive interpretation of the ML models is carried out via SHapley Additive exPlanations (SHAP) to understand the complex interplay of various features with N f and t r. The SHAP interpretation of the ML models reveals close agreement with the general creep equation and Wöhler curve of fatigue. The approach proposed in this study can accelerate the design of novel Ti-based alloys with desired properties.
How does social media use impact subjective well-being? Examining the suppressing role of Internet addiction and the moderating effect of digital skills

Introduction Previous studies have explored the impact of social media use on people’s subjective well-being, but there is a lack of discussion on the relationship between social media use, Internet addiction, and subjective well-being, and the research on the influence of digital skills on this relationship is not sufficient. This paper aims to fill these gaps. Based on the flow theory, this paper takes Chinese residents as the research object and uses CGSS 2017 data to analyze the impact of social media use on people’s subjective well-being. Methods Our study used multiple linear regression models for analysis. To test the hypotheses and the moderated mediation model, we adopted PROCESS models with 5000 bias-corrected bootstrap samples and 95% confidence intervals. All analyses were conducted using SPSS 25.0. Results The empirical analysis shows that social media use has a positive direct effect on subjective well-being, and Internet addiction plays a suppressing role in the relationship between social media use and subjective well-being. In addition, we found that digital skills moderated the positive effect of social media use on Internet addiction and the indirect effect of social media use on subjective well-being through Internet addiction. Discussion The conclusion of this paper supports our previous hypothesis. Besides, the theoretical contribution, practical significance, and limitations of this study are discussed based on the results of previous studies.