Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Gaussian scalar statistics in homogeneous turbulence

Results are presented of numerical simulations of passive scalar mixing in homogeneous, incompressible turbulent flows. These results are generated via the Linear Eddy Model (LEM) and Direct Numerical Simulation (DNS) of turbulent flows under a variety of different conditions. The nature of mixing and its response to the turbulence field is examined and the single-point probability density function (p.d.f.) of the scalar amplitude and the p.d. f.s of the scalar spatial-derivatives are constructed. It is shown that both Gaussian and exponential scalar p.d. f.s emerge depending on the parameters of the simulations and the initial conditions of the scalar field. Aided by the analyses of data, several reasons are identified for the non-Gaussian behaviour of the scalar amplitude. In particular, two mechanisms are identified for causing exponential p.d. f.s: (i) a non-uniform action of advection on the large and the small scalar scales, (ii) the nonlinear interaction of the scalar and the velocity fluctuations at small scales. In the absence of a constant non-zero mean scalar gradient, the behaviour of the scalar p.d. f. is very sensitive to the initial conditions. In the presence of this gradient, an exponential p.d. f. is not sustained regardless of initial conditions. The numerical results pertaining to the small-scale intermittency (non-Gaussian scalar derivatives) are in accord with laboratory experimental results. The statistics of the scalar derivatives and those of the velocity-scalar fluctuations are also in accord with laboratory measured results.
Nonthermal X-rays from supernova remnants

Abstract Since the discovery of nonthermal X-rays in the shell-type supernova remnant SN1006 almost 20 years ago, the field has developed considerably, owing significant progress to our understanding of particle acceleration. Key to the characterization of the nonthermal emission is the ability of current satellites, XMM-Newton and Chandra, to perform spatially resolved spectroscopy at a relatively small spatial scale. In this review, I intend to present the main contributions of the study of nonthermal X-rays from supernova remnants to the understanding of particle acceleration.
Non-additive geometry

We develop a language that makes the analogy between geometry and arithmetic more transparent. In this language there exists a base field $\mathbb{F}$, ‘the field with one element’; there is a fully faithful functor from commutative rings to $\mathbb{F}$-rings; there is the notion of the $\mathbb{F}$-ring of integers of a real or complex prime of a number field $K$ analogous to the $p$-adic integers, and there is a compactification of $\operatorname{Spec}O_K$; there is a notion of tensor product of $\mathbb{F}$-rings giving the product of $\mathbb{F}$-schemes; in particular there is the arithmetical surface $\operatorname{Spec} O_K\times\operatorname{Spec} O_K$, the product taken over $\mathbb{F}$.
Monte Carlo modeling of coherent scattering: influence of interference

In this study, the authors present Monte Carlo (MC) simulation results for the intensity and angular distribution of scattered radiation from cylindrical absorbers. For coherent scattering the authors have taken into account the effects of interference by using new molecular form factor data for the AAPM plastic materials and water. The form factor data were compiled from X-ray diffraction measurements. The new data have been implemented in the authors' Electron Gamma Shower (EGS4) Monte Carlo system. The hybrid MC simulation results show a significant influence on the intensity and the angular distribution of coherently scattered photons. It is concluded that MC calculations are significantly in error when interference effects are ignored in the model for coherent scattering. Especially for simulation studies of scattered radiation in collimated geometries, where small angle scattering will prevail, the coherent scatter contribution is highly overestimated when conventional form factor data are used.
An image localization system based on gradient Hough transform

With the development of image processing and computer vision, automatic target locating in computer vision has been matured and replaced the manual target locating in some fields. Complex background image localization has many important applications in traffic control and management, industrial assembly, industrial control, and other fields. In this paper, we built an image localization system based on Gradient Hough Transform applied in circular device detection and localization in industrial control. This system aimed to correcting the mechanical deviation by using image localization. This image localization system includes image preprocessing, edge extraction, circle detection, localization of the center of the circular device, deviation calculation and feedback. According to the experimental result, the accuracy of this method in the actual system less than 0.3mm, and the system can basically achieve the real-time detection.
Appliacation of Two-Dimensional PIV Method to the Wind Tunnel Experiment

Particle image velocimetry(PIV) is an optical method used to measure velocity and related properties of fluids with unique advantages of providing instantaneous and synchronous flow field information by non-intrusion.The velocity and related kinetic characteristics of the flow on the different cross-sections of a slender body model were studied by PIV in D-4 low speed wing tunnel of Beihang University.The results show that the PIV is a credible and accurate method to study the flow characteristics and is suitable to explore the complicated flow structure,such as vortex,turbulence,etc.Now,the PIV has developed to be one of the conventional test techniques to study the two-dimensional flow field in D-4 wind tunnel of Educational Ministry Key Laboratory in Beihang Univesity.
Nonsense mutation in CFAP43 causes normal-pressure hydrocephalus with ciliary abnormalities

Objective To identify genes related to normal-pressure hydrocephalus (NPH) in one Japanese family with several members with NPH. Methods We performed whole-exome sequencing (WES) on a Japanese family with multiple individuals with NPH and identified a candidate gene. Then we generated knockout mouse using CRISPR/Cas9 to confirm the effect of the candidate gene on the pathogenesis of hydrocephalus. Results In WES, we identified a loss-of-function variant in CFAP43 that segregated with the disease. CFAP43 encoding cilia- and flagella-associated protein is preferentially expressed in the testis. Recent studies have revealed that mutations in this gene cause male infertility owing to morphologic abnormalities of sperm flagella. We knocked out mouse ortholog Cfap43 using CRISPR/Cas9 technology, resulting in Cfap43-deficient mice that exhibited a hydrocephalus phenotype with morphologic abnormality of motile cilia. Conclusion Our results strongly suggest that CFAP43 is responsible for morphologic or movement abnormalities of cilia in the brain that result in NPH.
Flavonol Biosynthesis Genes and Their Use in Engineering the Plant Antidiabetic Metabolite Montbretin A1[OPEN]

Genes from the common garden plant montbretia enable a synthetic biology approach to the metabolic engineering of a plant anti-diabetic metabolite in Nicotiana benthamiana. The plant metabolite montbretin A (MbA) and its precursor mini-MbA are potential new drugs for treating type 2 diabetes. These complex acylated flavonol glycosides only occur in small amounts in the corms of the ornamental plant montbretia (Crocosmia × crocosmiiflora). Our goal is to metabolically engineer Nicotiana benthamiana using montbretia genes to achieve increased production of mini-MbA and MbA. Two montbretia UDP-dependent glycosyltransferases (UGTs), CcUGT1 and CcUGT2, catalyze the formation of the first two pathway-specific intermediates in MbA biosynthesis, myricetin 3-O-rhamnoside and myricetin 3-O-glucosyl rhamnoside. In previous work, expression of these UGTs in N. benthamiana resulted in small amounts of kaempferol glycosides but not myricetin glycosides, suggesting that myricetin was limiting. Here, we investigated montbretia genes and enzymes of flavonol biosynthesis to enhance myricetin formation in N. benthamiana. We characterized two flavanone hydroxylases, a flavonol synthase, a flavonoid 3′-hydroxylase (F3′H), and a flavonoid 3′5′-hydroxylase (F3′5′H). Montbretia flavonol synthase converted dihydromyricetin into myricetin. Unexpectedly, montbretia F3′5′H shared higher sequence relatedness with F3′Hs in the CYP75B subfamily of cytochromes P450 than with those with known F3′5′H activity. Transient expression of combinations of montbretia flavonol biosynthesis genes and a montbretia MYB transcription factor in N. benthamiana resulted in availability of myricetin for MbA biosynthesis. Transient coexpression of montbretia flavonol biosynthesis genes combined with CcUGT1 and CcUGT2 in N. benthamiana resulted in 2 mg g−1 fresh weight of the MbA pathway-specific compound myricetin 3-O-glucosyl rhamnoside. Additional expression of the montbretia acyltransferase CcAT1 led to detectable levels of mini-MbA in N. benthamiana.
THE EMPIRICAL BAYES TEST PROBLEM FOR SCALE EXPONENTIAL FAMILY: IN THE CASE OF NA SAMPLES

By using the kernel-type density estimation in the case of identically distributed and negatively associated (NA) samples, the empirical Bayes test rules for the scale exponential family are constructed, and the asymptotically optimal property is obtained. It is shown that the convergence rates of the proposed EB test rules can arbitrarily close to O (n-) under suitable conditions. Finally, an example about the main results of this paper is given.
EMBEDD-ER: EMBEDDing Educational Resources Using Linked Open Data

: There are a lot of educational resources publicly available online. Recommender systems and information retrieval engines can help learners and educators navigate in these resources. However, the available educational resources differ in format, size, type, topics, etc. These differences complicate their use and manipulation which raised the need for having a common representation for educational resources and texts in general. Efforts have been made by the research community to create various techniques to homogeneously represent these resources. Although these representations have achieved incredible results in many tasks, they seem to be dependent on the writing style and not only on the content. Furthermore, they do not generate representations that reﬂect a semantic representation of the content. In this work, we present a new task-agnostic method (EMBEDD-ER) to generate representations for educational resources based on document annotation and Linked Open Data (LOD). It creates representations that are focused on the content, compact, and can be generalized to unseen resources without requiring extra training. The resulting representations encapsulate the information found in the resources and project similar resources closer to one another than to non-similar ones. Empirical tests have shown promising results both visually and in a subject classiﬁcation task.
Photonics-electronics Convergence Technologies for Disaggregated Computing

using photonics instead of electronics for the connection between boards (printed circuit boards) in a rack and the connection between LSIs on a board using the interconnect model. Figure 3(a) is an electrical model in which an LSI is mounted on a board and a high-speed signal converted from the clock frequency of the LSI
The Optimal Open Innovation Strategy with Science-based Partners for Venture Firm’s Innovation Capabilities: Focusing on Innovation Modes

This study explores the difference in the effects of innovation modes on innovation capabilities dividing various collaborative activities conducted by venture firms with science-based partners into innovation modes. Venture firms that relatively lack the resources and capabilities for innovation perform various collaborative activities such as marketing support and management support as well as research and development collaboration with science-based partners. However, this has not received proper attention in the literature on open innovation. We classified venture firms according to innovation modes and compared each group through a multi-treatment model. As a result, we found that there was a difference in the impact on innovation capability according to the innovation mode with science-based partners. This study contributes to enriching the empirical bodies of open innovation research. In addition, it suggests that there is a need for a portfolio approach considering the innovation modes by establishing policies at the level of collaborative activities in supporting the collaboration of venture firms.
On regime-switching European option pricing

Abstract The concern of this article is to derive a regime switching model that can be utilized to price European call options for a financial market that exhibits structural changes with time. The model is formulated based on the fact that the underlying asset process is described by a geometric Brownian motion that is modulated by a continuous-time Markov chain with two regimes. Moreover, by an application of the change of measure technique, an option price is derived under the risk neutral valuation and the model parameter estimates is performed by use of the maximum likelihood estimation. The model implementation is carried out by utilizing the Russell 2000 and Facebook in dices data sets. The model results are compared with that of the Black-Scholes model in order to establish the model with better results in terms of predicting the European call option prices. In general, the data sets have common characteristics of financial time series across the regimes and the volatility process spends longer time in regime 2 than it stays in regime 1. The predicted call option prices from both models are more or less similar across the market indices; however, the results of the Black-Scholes model are a bit closer to the market prices than that of the regime-switching model across the two markets. Therefore, the Black-Scholes model slightly gives better results for the Russell 2000 and Facebook indices data sets as compared with the RS model.
The largest critical point in the zero-one -law

The largest value of is established for which the random graph does not obey the zero-one -law for first-order properties. As was already known, the zero-one -law is valid for all with the exception of , . For the law fails. In this work, it is shown that the law is valid for . Bibliography: 17 titles.
Moment methods in extremal geometry

In this thesis we develop techniques for solving problems in extremal geometry. We give an infinite dimensional generalization of moment techniques from polynomial optimization. We use this to construct semidefinite programming hierarchies for approximating optimal packing densities and ground state energies of particle systems. For this we define topological packing graphs as an abstraction for the graphs arising from geometric packing problems, and we prove results concerning convergence and strong duality. We use harmonic analysis to perform symmetry reduction and reduce to a finite dimensional variable space in the optimization problems. For this we explicitly work out the harmonic analysis for kernels on spaces consisting of subsets of another space. We show how sums of squares characterizations from real algebraic geometry can be used to reduce the infinitely many constraints to finitely many semidefinite constraints, where we focus in particular on numerical conditioning and symmetry reduction. We perform explicit computations for concrete problems: We give new bounds for binary spherical cap packings, binary sphere packings, and classical sphere packing problems. This can be used, for instance, to give a simple optimality proof of a binary spherical cap packing. We compute the second step of our hierarchy where the numerical results suggest the bound is sharp for the 5-particle case of the Thomson and related problems. This is the first time a 4-point bound has been computed for a continuous problem.
Strong Radiation-Damping Effects in a Gamma-Ray Source Generated by the Interaction of a High-Intensity Laser with a Wakefield-Accelerated Electron Beam

A number of theoretical calculations have studied the effect of radiation-reaction forces on radiation distributions in strong-field counterpropagating electron-beam-laser interactions, but could these effects-including quantum corrections-be observed in interactions with realistic bunches and focusing fields, as is hoped in a number of soon-to-be-proposed experiments? We present numerical calculations of the angularly resolved radiation spectrum from an electron bunch with parameters similar to those produced in laser-wakefield-acceleration experiments, interacting with an intense, ultrashort laser pulse. For our parameters, the effect of radiation damping on the angular distribution and energy distribution of photons is not easily discernible for a realistic moderate-emittance electron beam. However, experiments using such a counterpropagating beam-laser geometry should be able to measure these effects using current laser systems through measurement of the electron-beam properties. In addition, the brilliance of this source is very high, with peak spectral brilliance exceeding 10(29) photons s(-1) mm(-2) mrad(-2) (0.1% bandwidth)(-1) with an approximately 2% conversion efficiency and with a peak energy of 10 MeV.
An M-matrix based Efficient Algorithm for Model Predictive Control of Input Constrained Linear Systems

Abstract This paper discusses an on-line approach to Model Predictive Control (MPC) of input constrained linear systems. First, it is shown that an example of MPC with one-side input constraints is reduced into the Linear Complementarity (LC) Problem with M -matrix, which can be more efficiently solved by the existing special algorithm than the other general algorithms such as the Lemke method. However, in the case of both-side input constraints, which is more practical from the control engineering points of view, it cannot be reduced into the LC problem with M -matrix. Thus, next, a new algorithm for the case of both-side input constraints is proposed in this paper.
Use of Monte Carlo Simulation Methods to Improve X-ray Detector Response Function

An accurate Si (Li) detector response function is needed for many applications such as Xray spectroscopy and elemental analysis. A semi-empirical detector response function using Gaussian peak models and exponential tails was used to characterize the detector response functions. And the use of Monte Carlo method to simulate the detector response has been developed to predict both spectral shape characteristics and detector efficiency for any incident X-ray energy. The benchmark of the detector response function on Si (Li) detector was performed on elements Al, Si, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, and Pb, which are excited by a 17.5 keV mono-energetic X-ray source using a micro-focused X-ray spectroscopy analyzer. The semi-empirical parameters are optimized by nonlinear regression with experimental spectra of pure-element samples. The model fitted results are presented and indicate good agreement with experimental data. The detector response functions are pre-calculated with high statistical precision. Consequently, it can be used in X-ray spectroscopy and elemental analysis with great accuracy.
The moderating role of ICT on the relationship between foreign direct investment and the quality of environment in selected African countries

Abstract This empirical study examines the role of information and communication technology (ICT) in the relationship between FDI and environmental quality for six leading African economies from 1970 to 2020. The second-generation tests are used to determine the stationarity level of the variables. Furthermore, the Westerlund panel cointegration test confirms cointegration among the variables. For long-run association, CS-ARDL, which resolved the consequences of heterogeneity and cross-sectional dependency is used. The results of the study reveal that ICT, FDI, economic growth, and financial development degrade environmental quality. The interacting effect of ICT and FDI (ICT*FDI) leads to escalation of CO2 emissions thereby deteriorating the quality of the environment. The study recommends that African countries should promote FDI to support the inflow of green technologies to enhance environmental quality. The implementation of environmentally sustainable technology would help improve the quality of the environment, increase sustainability in the long term and conserve resources for future generation.
Adaptation and validation of the Questionnaire on Exposure to Violence in young people

For just over a decade, interpersonal violence has impacted the daily life of the Mexican population, particularly adolescents. With the aim of having an instrument that assesses the perception of violent events in different contexts, the objective was to adapt and validate the “Questionnaire exposure to violence” (CEV), to which was added a group of items related to exposure to violent events through the mass media (TV, social networks, and streaming). The findings show that the CEV has a high psychometric quality in the Mexican adolescent population, and that the inclusion of items related to the perception of the occurrence of violent events through the mass media proved to be useful to evaluate violence on this scale. The model obtained in the confirmatory factor analysis was verified by means of absolute indices (GFI, AGFI, CFI, RMSEA and NFI Delta1), which were above the minimum acceptable, showing two clearly defined factors: the perception of violence in physical contexts and in mass media contexts.
Notes on Social Measurement: Historical and Critical.

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Time-dependent Iterative Imputation for Multivariate Longitudinal Clinical Data

Missing data is a major challenge in clinical research. In electronic medical records, often a large fraction of the values in laboratory tests and vital signs are missing. The missingness can lead to biased estimates and limit our ability to draw conclusions from the data. Additionally, many machine learning algorithms can only be applied to complete datasets. A common solution is data imputation, the process of filling-in the missing values. However, some of the popular imputation approaches perform poorly on clinical data. We developed a simple new approach, Time-Dependent Iterative imputation (TDI), which offers a practical solution for imputing time-series data. It addresses both multivariate and longitudinal data, by integrating forward-filling and Iterative Imputer. The integration employs a patient, variable, and observation-specific dynamic weighting strategy, based on the clinical patterns of the data, including missing rates and measurement frequency. We tested TDI on randomly masked clinical datasets. When applied to a cohort consisting of more than 500,000 patient observations from MIMIC III, our approach outperformed state-of-the-art imputation methods for 25 out of 30 clinical variables, with an overall root-mean-squared-error of 0.63, compared to 0.85 for SoftImpute, the second best method. MIMIC III and COVID-19 inpatient datasets were used to perform prediction tasks. Importantly, these tests demonstrated that TDI imputation can lead to improved risk prediction.