Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk contagion among financial players modelled by a SIR model with time delay

We revisit a mathematical approach using epidemiological models to describe risk contagion and propagation among ﬁnancial players and markets. The link between the ﬁnancial system and the ecosystem is explained by a SIR model with time delay. We aim to apply this on more complex ﬁnancial systems, where cryptocurrencies, new partici-pants and traditional ﬁnancial operators play together. This work represents a starting point for a deep stochastic analysis of the contagion term inﬂuence over risk contagion dynamics
Impacts of a Mobile Childbirth Educational Game on Learning Achievement, Self-Efficacy and Postnatal Depression for Puerperal Women

Giving birth has huge meaning in the life of individual mothers. Childbirth education is important for mothers to build up the basic knowledge of newborn care. Learning more knowledge of childbirth would help to reduce postnatal depression in puerperal women. However, few studies have discussed technology-enhanced postnatal childbirth education, especially using mobile technology and effective learning strategies such as gaming in this important educational field. Therefore, in this study, a childbirth educational game was proposed to provide various gaming scenarios and childbirth learning materials. A quasi-experiment will be conducted to verify the effectiveness of the mobile gaming system. Eighty puerperal women will participate in this experiment and be divided into two groups to compare the proposed approach and the conventional childbirth education. It is expected that the proposed approach will enhance the learning achievement, learning perception, and technology acceptance, and reduce the degree of postnatal depression of puerperal women.
The Curve-Fitting Problem: An Objectivist View

Model simplicity in curve fitting is the fewness of parameters estimated. I use a vector model of least squares estimation to show that degrees of freedom, the difference between the number of observed parameters fit by the model and the number of explanatory parameters estimated, are the number of potential dimensions in which data are free to differ from a model and indicate the disconfirmability of the model. Though often thought to control for parameter estimation, the AIC and similar indices do not do so for all model applications, while goodness of fit indices like chi-square, which explicitly take into account degrees of freedom, do. Hypothesis testing with prespecified values for parameters is based on a metaphoric regulative subject/object schema taken from object perception and has as its goal the accumulation of objective knowledge.
Kernel and stochastic expectation maximization fusion for target detection in hyperspectral imagery

In this paper, we present a new algorithm for target detection using hyperspectral imagery. The proposed algorithm is inspired by the outstanding performance of nonlinear RX-algorithm and the robustness of the stochastic expectation maximization (SEM) algorithm. The traditional technique of using SEM algorithm for target detection in hyperspectral imagery is associated with dimensionality reduction of the input data using binning or principal components analysis (PCA) algorithm. Although, the data reduction of the input data is enforced to reduce the computational burden on SEM algorithm, but it affects the results of target detection, especially the challenging one, due to not using the entire information of the potential targets. To facilitate detection of the target by using the entire targets information and simultaneously reducing the computational burden on SEM algorithm, we propose a new scheme for data reduction based on using Kernels. Kernel-based input data reduction is a nonlinear filtering technique in which the input data are mapped to the feature space where most of the background data is filtered using an easily selected threshold. Then, Gaussian mixture model is generated for the reduced input-data and SEM algorithm is employed to estimate the model parameters and to classify that input data. Finally, we allocated the target's class and isolated the target pixels. The proposed scheme for fusion the kernel with SEM algorithm has been tested using real life hyperspectral imagery and the results show superior performance compared to alternate algorithms.
The root meristem growth factor BrRGF6 positively regulates Chinese cabbage to infection of clubroot disease caused by Plasmodiophora Brassicae

Abstract Chinese cabbage has a high annual demand in China. However, clubroot disease caused by the infection of Plasmodiophora brassicae seriously affects its yield. Transcriptome analysis identified a root meristem growth factor 6 (BrRGF6) as significantly up-regulated in Chinese cabbage roots infected with Plasmodiophora brassicae. Quantitative reverse-transcription polymerase chain reaction and in situ hybridization analysis showed higher BrRGF6 expression in susceptible materials than in resistant materials. After Plasmodiophora brassicae infection, BrRGF6 expression was significantly up-regulated, especially in susceptible materials. Gene function analysis showed that the roots of Arabidopsis mutant rgf6 grew faster than the wild-type, and delayed the infection progress of Plasmodiophora brassicae. A Protein, nuclear transcription factor Y subunit C (BrNF-YC), was screened from yeast two-hybrid library of Chinese cabbage induced by Plasmodiophora brassicae, and verified to interact with BrRGF6 by yeast two-hybrid co-transfer. Yeast one-hybrid and β-Glucuronidase activity analysis showed that BrNF-YC could directly bind to and strongly activate the promoter of BrRGF6. Transgenic verification showed that BrRGF6 or BrNF-YC silenced Chinese cabbage significantly decreased the expression of BrRGF6, accelerated root development, and reduced incidence of clubroot disease. However, after overexpression of BrRGF6 or BrNF-YC, the phenotype showed a reverse trend. Therefore, BrRGF6 silencing accelerated root growth and enhanced resistance to clubroot disease, which was regulated by BrNF-YC.
STUNTING PROGRAM STRATEGY EFFECTIVE IN BUALO VILLAGE

The purpose of the study was to determine the form of an effective stunting management program strategy in Bualo Village. This research is a type of descriptive research with a qualitative approach. Data collection methods used in this study were observation, in-depth interviews (indept interviews) and documentation. The data analysis technique used in qualitative research is carried out before entering the field, during the field and after completing the field. The results showed that the organizational strategy in stunting handling was considered to be very supportive of stunting handling and was considered to have supported effective stunting management. Program strategies in handling stunting were less capable in handling stunting in reducing stunting rates in Bualo Village. and reducing the stunting rate in Bualo Village. The institutional strategy in handling stunting is maximized because it is still improving the handling of stunting in Bualo Village.
Analysis of Nonrandomly Censored Ordered Categorical Longitudinal Data from Analgesic Trials

Abstract A clinical trial of an analgesic agent compares pain relief scores over time among groups of patients. All subjects experience the same painful procedure, but different subjects are given different randomly assigned doses of active agent or placebo when they first request it. The data are short individual time series of ordered categorical pain relief scores subsequent to dosing. Nonrandom right censoring may be present because patients can elect to remedicate with an active agent if their pain relief is insufficient. The trial is meant to address two questions: (a) Is there proof that the drug relieves pain? If so, (b) What dosage patterns should be investigated further, or recommended for use by a typical patient? Marginal models of human pharmacology are basically empirical models, and although an analysis of a study based on such a model can adequately address the first question, such is not the case for the second question, because this question requires extrapolation to untested dosing patt.
Temperature-insensitive strain sensor with polarization-maintaining photonic crystal fiber based Sagnac interferometer

A fiber-optic strain sensor is demonstrated by using a short length of polarization-maintaining photonic crystal fiber (PM-PCF) as the sensing element inserted in a Sagnac loop interferometer. Spectrum shift in response of strain with a sensitivity of 0.23pm∕μe is achieved, and the measurement range, by stretching the PM-PCF only, is up to 32me. Due to the ultralow thermal sensitivity of the PM-PCF, the proposed strain sensor is inherently insensitive to temperature, eliminating the requirement for temperature compensation.
Biomechanics: Mechanical Properties of Living Tissues, 2nd ed.

This book is the second edition of the first volume in a series of three volumes by Y. C. Fung on biomechanics. The first edition was published in 1981. The second volume in the series was Biodynamics: Circulation (1984) and the third was Biomechanics: Motion, Flow, Stress, and Growth (1990). In these three books Professor Fung describes the constitutive behavior of biological tissues, the functional mechanics of the body's organs and related engineering problems. The series of three volumes summarizes the application of an enormously wide spectrum of mechanics and thermodynamics to physiology and the engineering of biologically related problems. The mechanics applied includes rigid-body mechanics, mechanical vibration theory, viscous and perfect fluid theory, linear and nonlinear elasticity and viscoelasticity, wave propagation in fluid and solid media, multiphase mixture theory, chemical thermodynamics, and numerous subtopics of mechanics. The problems addressed cover the spectrum from birds flying and fish swimming to oxygen transport, human impact tolerance, cell behavior, and the engineering of activities at the cellular level.
Strong Generalized Speech Emotion Recognition Based on Effective Data Augmentation

The absence of labeled samples limits the development of speech emotion recognition (SER). Data augmentation is an effective way to address sample sparsity. However, there is a lack of research on data augmentation algorithms in the field of SER. In this paper, the effectiveness of classical acoustic data augmentation methods in SER is analyzed, based on which a strong generalized speech emotion recognition model based on effective data augmentation is proposed. The model uses a multi-channel feature extractor consisting of multiple sub-networks to extract emotional representations. Different kinds of augmented data that can effectively improve SER performance are fed into the sub-networks, and the emotional representations are obtained by the weighted fusion of the output feature maps of each sub-network. And in order to make the model robust to unseen speakers, we employ adversarial training to generalize emotion representations. A discriminator is used to estimate the Wasserstein distance between the feature distributions of different speakers and to force the feature extractor to learn the speaker-invariant emotional representations by adversarial training. The simulation experimental results on the IEMOCAP corpus show that the performance of the proposed method is 2–9% ahead of the related SER algorithm, which proves the effectiveness of the proposed method.
A RATIONAL ALGEBRAIC FORMULATION OF THE PROBLEM OF RELATIVE ORIENTATION

The problem of relative orientation involves the determination of the elements of at least one orthogonal matrix. Hitherto a difficulty has arisen in that, in this context, orthogonal matrices have not been expressed in terms of three independent parameters without the use of circular functions. In this paper a more tractable form of the orthogonal matrix is used to set up a rational algebraic equation expressing the relative orientation condition. This equation turns out to be of the third order in the five unknowns. A set of such equations may be solved by a rapidly converging process of iteration.
Simulation of metal forming processes with finite element methods

The traditional Lagrangian and Eulerian formulations in finite elements posses some inherent difficulties when used in simulation of metal-forming processes or general finite strain problems. A more general method of formulation, the Arbitrary Lagrangian–Eulerian (ALE), is developed to overcome such difficulties. A brief description of the ALE formulation is given with emphasis on the underlying differences between the developed formulation and existing ones in the literature. Some numerical algorithms for incorporating large deformation effect are presented. Typical metal-forming processes such as strip rolling, metal extrusion and punch forging are simulated using the ALE formulation and results are compared with available numerical and experimental ones. Special attention is given to numerical characteristics of the simulation, application of boundary condition and their effect on the results. Copyright © 1999 John Wiley & Sons, Ltd.
Influence Maximization via Representation Learning

Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges lie on the one hand on the computational demand of the algorithmic solution which restricts the scalability, and on the other the quality of the predicted influence spread. In this work, we propose IMINFECTOR (Influence Maximization with INFluencer vECTORs), a method that aspires to address both problems using representation learning. It comprises of two parts. The first is based on a multi-task neural network that uses logs of diffusion cascades to embed diffusion probabilities between nodes as well as the ability of a node to create massive cascades. The second part uses diffusion probabilities to reformulate influence maximization as a weighted bipartite matching problem and capitalizes on the learned representations to find a seed set using a greedy heuristic approach. We apply our method in three sizable networks accompanied by diffusion cascades and evaluate it using unseen diffusion cascades from future time steps. We observe that our method outperforms various competitive algorithms and metrics from the diverse landscape of influence maximization, in terms of prediction precision and seed set quality.
A multimodal approach to automated generation of radiology reports using contrastive learning (SHORT PAPER)

In the present paper, we present a preliminary and ongoing work concerning the problem of generating a suitable diagnostic report from radiology (x-ray) images. The task is tackled with a contrastive learning approach, where radiology images and textual reports are embedded in a common space, with the goal of getting similar (close in space) embeddings between a given image and the corresponding report. Once the embeddings for an image and the corresponding textual report are generated (through suitable fine-tuned models), we propose to feed them to a contrastive learning engine in such a way that image and textual embeddings are pushed close in the embedding space if image and text are related, while they are moved away otherwise. Preliminary analysis shows promising results in terms of effectiveness of the contrastive learning approach, but also suggest relevant issues to be investigated such as the importance of context and the role of suitable encoder/decoder modules to properly deal with the textual generation.
EFFECT OF DIETARY SUPPLEMENTATION WITH CLOMIPHENE CITRATE ON SOME PRODUCTIVE AND PHYSIOLOGICAL TRAITS OF LAYING JAPANESE QUAIL

This study was conducted to evaluate the effect of supplementation different levels of Clomiphene citrate to the diet of female quail on productive and some physiological traits. The experiment was conducted in a commercial farm from 2/3/2021 to 24/5/2021 (3 months). Females aged 50 weeks (160 females) were randomly distributed into four treatments, and each treatment included four replicates (10 birds for each replicate). The treatments were divided into a control group (T1; basic diet only), second treatment (T2; basic diet + 0.25 mg / female / day), third treatment (T3; basic diet + 0.50 mg / female / day, fourth treatment (T4; basic diet + 0.75 mg / female / day Clomiphene citrate). The results of the study indicated a significant improvement in favor of the addition treatments compared with the control treatment when calculating the average (%) of eggs production, the average of eggs weight (gm), the average of eggs mass (gm/female/day), and cumulative egg number (egg/female). As for the physiological characteristics, the results shown a high concentration of calcium and phosphorous in the blood serum within the additive treatments compared with the control While there were no significant differences observed among treatments in total protein, albumin, globulin, and cholesterol. It could be conclude from this study that the addition of Clomiphene citrate to the diet improved the reproductive and physiological characteristics of female quail.
A numerical method for the study of solid-liquid mass transfer in turbulent flows

A numerical method for the study of high-Schmidt number mass transfer close to solid boundaries is presented. Computational Fluid Dynamics with the Large Eddy Simulations and the dynamic Smagorinsky model for the subgris scale stress tensor were used. Periodic boundary conditions were used to obtain proper boundary conditions for the LES simulation. A fine mesh was used in the bulk of the flow and a extremely fine mesh (y+ = 1/40) on a part of the surface where the diffusional flux was evaluated. Model validation shows that the teoretical y+ dependences were obtained for the fluctuations paralell to the surface and a somewhat lower y+ depencence was obtained for the much smaller perpendicular fluctuation. It is also shown that DNS resolution was obtained in the extremely fine mesh. The usefuleness of the modeling method is exemplified with a simulation of species concentration and by particle tracking the influence of the turbulence on the fluid elements near the walls at y+ < 1.
In-Diffusion and Annealing Processes of Substitutional Nickel Atoms in Dislocation-Free Silicon

In-diffusion and annealing processes of substitutional nickel atoms in dislocation-free silicon are studied in the temperature range from 940 to 1020°C to distinguish between the site exchange mechanisms of nickel atoms. The concentration of substitutional nickel atoms varies with time according to the theoretical prediction of the dissociative mechanism. It is found that the in-diffusion and annealing rates are accelerated by nickel precipitation in the bulk. The relationship between the time constant of the two processes and the volume density of the precipitates is discussed.
Finite-time stochastic stabilisation of Markovian jump non-linear quadratic systems with partially known transition probabilities

This paper studies the problems of the finite-time stochastic boundedness (FTSB) and stabilisation for a class of discrete-time Markovian jump non-linear quadratic systems with incomplete information on transition probabilities. It is shown that the FTSB of the system can be ensured under some sufficient conditions derived. As a corollary, some sufficient conditions of the finite-time stochastic stability for the non-linear quadratic systems without exogenous disturbance are also established. Based on these results, stabilising mode-dependent state feedback controllers are designed for Markovian jump non-linear quadratic systems with/without exogenous disturbance, respectively. Consequently, the finite-time stochastic stabilisation problem is resolved. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed theory.
ABDF 2 ‑Net: an adaptive bi‑directional features fusion network for real‑time detection of threat object

To achieve automatic detection of threat objects for X-ray baggage screening, we propose an adaptive bi-directional features fusion network ( ABDF 2 -Net) to detect threat objects on X-ray images. In ABDF 2 -Net, an adaptive bi-directional feature fusion module ( ABDF 2 M ) is introduced to fuse the multi-scale features from two directions, and the adaptive function is used to control the features passing rate. Besides, an atrous convolutional pyramid pooling (ACPP) is employed to capture global contextual information, which can provide global semantic guidance for multi-scale features. Finally, the fused multi-scale features are used to predict the final detection results through prediction modules. Experiments on the GDXray database demonstrate the effectiveness and superiority of our proposed method against the other four object detection methods.
PMU-extended Hardware ROP Attack Detection

Return Oriented Programming is one of the major challenges for software security nowadays. It can bypass Data Execution Prevention (DEP) mechanism by chaining short instruction sequences from existing code together to induce arbitrary code execution. Existing defenses are usually trade-offs between practicality, security, and performance. In this paper, we propose PMUe, a low-cost hardware ROP detection approach that detects ROP attack based on three inherent properties of ROP. It is transparent to user applications and can be regarded as a small extension to existing Performance Monitoring Unit in commodity processors. Our evaluation demonstrates that PMUe can effectively detect ROP attack with negligible performance overhead.
Radical d'une algèbre symétrique à gauche

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Dynamic mechanical characterization of the heat-induced formation of fractal globular protein gels

The frequency dependence of the shear modulus of heat denatured β-lactoglobulin gels at pH 7 at 0.1 M NaCl was studied during the gelation process. It can be characterized by two modes. The fast mode at different protein concentrations is successfully described in terms of the fractal gel model. The slow mode is characterized by a power law frequency dependence and is attributed to structural relaxation of the elastic backbone. The power law exponent is independent of heating time and decreases linearly with decreasing concentration. The evolution of the dynamic mechanical properties during gelation is independent of the heating temperature although the gelation rate increases strongly with increasing temperature.
On Geopotential Data and Ellipticity of the Balance Equation: A Data Study

Abstract One year of geopotential data obtained from the National Meteorological Center and the National Center for Atmospheric Research are diagnosed for the occurrence of non-elliptic regions with respect to the balance equation. The highest frequencies of such occurrences appear at 200 mb over the subtropical oceans where there are few radiosonde observations. Substantial 200 mb frequencies are also found over the United States in the summer season above a reliable data net. A diagnosis of flow divergence implied for the non-elliptic data by a theoretical analysis of Paegle and Paegle (1974) produces values greatly in excess of. typical observations. This suggests that the gridding of the data by objective analysis may not have been adequate and/or that the aforementioned theory overestimates flow divergence in these regions. It is likely that non-elliptic data are important for initialization of primitive equation forecast models. It may be inferred that greater data accuracy, as well as better initia.