Miracle-dz/UniFAR-FTUs huggingface dataset:
A real-time identification method of network structure in complex network systems

This paper develops a real-time identification algorithm for structures of network systems. We have already proposed a structure identification method for nonlinear network systems with nonlinear couplings by combining Koopman operator theory and a sparse identification method. However, the proposed method is only applicable for offline use under the assumption that the state of an isolated system is available. To relax this assumption, this paper shows that the dynamical model of isolated systems can be extracted from measured data of interconnected systems under some weak restrictions on network structures. We also modify the identification algorithm to obtain real-time identification results with fewer calculation resources and analyse the convergence of the proposed methods. Numerical examples demonstrate that the proposed method can identify network structures in real-time and detect changes in networks correctly.
Risk Assessment of Basic Social Services in the Province of Bukidnon

This study conducted a risk assessment and formulated a model to measure the disaster risk reduction of selected flood-prone and landslide-prone barangays in the Province of Bukidnon. It specifically focused on the basic social services on food, water, electricity, and health. Primary and secondary data were used to establish hazard and population density maps to determine the exposure of communities during disasters. Further, the adaptive capacity (CA) of a local government unit (LGU)to respond to disasters was computed. Key findings revealed that exposed communities could be classified into various levels of susceptibility. Using the model, results revealed that the reduction of risk depends on the adaptive capacities and risks of LGUs. Further, efficiency to address risk is a ratio between the adaptive capacity and risk. Thus, the higher the adaptive capacity of LGUs would mean better efficiency in disaster risk reduction.
A zonal method for modeling powered-lift aircraft flow fields

A zonal method for modeling powered-lift aircraft flow fields is based on the coupling of a three-dimensional Navier-Stokes code to a potential flow code. By minimizing the extent of the viscous Navier-Stokes zones the zonal method can be a cost effective flow analysis tool. The successful coupling of the zonal solutions provides the viscous/inviscid interations that are necessary to achieve convergent and unique overall solutions. The feasibility of coupling the two vastly different codes is demonstrated. The interzone boundaries were overlapped to facilitate the passing of boundary condition information between the codes. Routines were developed to extract the normal velocity boundary conditions for the potential flow zone from the viscous zone solution. Similarly, the velocity vector direction along with the total conditions were obtained from the potential flow solution to provide boundary conditions for the Navier-Stokes solution. Studies were conducted to determine the influence of the overlap of the interzone boundaries and the convergence of the zonal solutions on the convergence of the overall solution. The zonal method was applied to a jet impingement problem to model the suckdown effect that results from the entrainment of the inviscid zone flow by the viscous zone jet. The resultant potential flow solution created a lower pressure on the base of the vehicle which produces the suckdown load. The feasibility of the zonal method was demonstrated. By enhancing the Navier-Stokes code for powered-lift flow fields and optimizing the convergence of the coupled analysis a practical flow analysis tool will result.
Inhibition of TRIF-Dependent Inflammation Decelerates Afterload-Induced Myocardial Remodeling

Pressure-overload-induced cardiac hypertrophy represents one cause of the development of heart failure. The aim of this study is to characterize the influence of the TIR-domain-containing adapter-inducing interferon-β (TRIF) during afterload-induced myocardial remodeling. After trans-aortic constriction (TAC), cardiac pressure overload leads to an early increase in MyD88- (Myeloid differentiation primary response gene 88) and TRIF-dependent cytokines. The maximum cytokine expression appeared within the first week and decreased to its control level within five weeks. While cardiomyocyte hypertrophy was comparable, the myocardial accumulation of the inflammatory cells was lower in TRIF−/−mice. At d7, TRIF deficiency reduced transcription factors and TRIF-dependent cytokines. Through the modulation of the TGF-β-signaling pathway and anti-fibrotic microRNAs, TRIF was involved in the development of interstitial fibrosis. The absence of TRIF was associated with a decreased expression of proapoptotic proteins. In echocardiography and working heart analyses, TRIF deficiency slowed left-ventricular wall thickening, myocardial hypertrophy, and reduces the ejection fraction. In summary, TRIF is an important adapter protein for the release of inflammatory cytokines and the accumulation of inflammatory cells in the early stage of maladaptive cardiac remodeling. TRIF is involved in the development of cardiac fibrosis by modulating inflammatory and fibrotic signal transduction pathways.
Using the Diamagnetic Coefficients to Estimate the Reduced Effective Mass in 2D Layered Perovskites: New Insight from High Magnetic Field Spectroscopy

In this work, the current state of research concerning the determination of the effective mass in 2D layered perovskites is presented. The available experimental reports in which the reduced effective mass μ has been directly measured using magneto-absorption spectroscopy of interband Landau levels are reviewed. By comparing these results with DFT computational studies and various other methods, it is concluded that depending on the approach used, the μ found spans a broad range of values from as low as 0.05 up to 0.3 me. To facilitate quick and reliable estimation of μ, a model is proposed based solely on the available experimental data that bypass the complexity of interband Landau level spectroscopy. The model takes advantage of the μ value measured for (PEA)2PbI4 and approximates the reduced effective mass of the given 2D layered perovskites based on only two experimental parameters—the diamagnetic coefficient and the effective dielectric constant. The proposed model is tested on a broad range of 2D layered perovskites and captures well the main experimental and theoretical trends.
Insights into Natufian Social Identity: A Case Study from the Graveyard of Hayonim Cave

Summing up the data deriving from the Natufian burials at Hayonim Cave which incorporates information pertaining to the last grave uncovered on site (Grave XVII), the paper endeavours to understand the role of burials within the evolving Natufian society at large. It seems that certain sites—Hayonim Cave being a case in point—served as special localities, used by a particular group as a burial ground all through the Natufian time-span (i.e. for more than 3000 years). Members of that group returned to the cave again and again in order to bury their dead, being aware of the location of the preceding graves, apparently retaining a long-term memory of their burial practices. At the same time, at least some burials provide evidence for inter-group ties, as evident through particular similarities between certain burials in two distinct Natufian sites, Hayonim Cave and Eynan (‘Ain Mallaha). Clearly, retention of mortuary practices played a significant role in consolidating and preserving social cohesion in the Natufian society.
Statistical Inference: The Minimum Distance Approach

Introduction General Notation Illustrative Examples Some Background and Relevant Definitions Parametric Inference based on the Maximum Likelihood Method Hypothesis Testing by Likelihood Methods Statistical Functionals and Influence Function Outline of the Book Statistical Distances Introduction Distances Based on Distribution Functions Density-Based Distances Minimum Hellinger Distance Estimation: Discrete Models Minimum Distance Estimation Based on Disparities: Discrete Models Some Examples Continuous Models Introduction Minimum Hellinger Distance Estimation Estimation of Multivariate Location and Covariance A General Structure The Basu-Lindsay Approach for Continuous Data Examples Measures of Robustness and Computational Issues The Residual Adjustment Function The Graphical Interpretation of Robustness The Generalized Hellinger Distance Higher Order Influence Analysis Higher Order Influence Analysis: Continuous Models Asymptotic Breakdown Properties The alpha-Influence Function Outlier Stability of Minimum Distance Estimators Contamination Envelopes The Iteratively Reweighted Least Squares (IRLS) The Hypothesis Testing Problem Disparity Difference Test: Hellinger Distance Case Disparity Difference Tests in Discrete Models Disparity Difference Tests: The Continuous Case Power Breakdown of Disparity Difference Tests Outlier Stability of Hypothesis Tests The Two Sample Problem Techniques for Inlier Modification Minimum Distance Estimation: Inlier Correction in Small Samples Penalized Distances Combined Distances o-Combined Distances Coupled Distances The Inlier-Shrunk Distances Numerical Simulations and Examples Weighted Likelihood Estimation The Discrete Case The Continuous Case Examples Hypothesis Testing Further Reading Multinomial Goodness-of-fit Testing Introduction Asymptotic Distribution of the Goodness-of-Fit Statistics Exact Power Comparisons in Small Samples Choosing a Disparity to Minimize the Correction Terms Small Sample Comparisons of the Test Statistics Inlier Modified Statistics An Application: Kappa Statistics The Density Power Divergence The Minimum L2 Distance Estimator The Minimum Density Power Divergence Estimator A Related Divergence Measure The Censored Survival Data Problem The Normal Mixture Model Problem Selection of Tuning Parameters Other Applications of the Density Power Divergence Other Applications Censored Data Minimum Hellinger Distance Methods in Mixture Models Minimum Distance Estimation Based on Grouped Data Semiparametric Problems Other Miscellaneous Topics Distance Measures in Information and Engineering Introduction Entropies and Divergences Csiszar's f-Divergence The Bregman Divergence Extended f-Divergences Additional Remarks Applications to Other Models Introduction Preliminaries for Other Models Neural Networks Fuzzy Theory Phase Retrieval Summary
Platelet-Derived Exosomes in Atherosclerosis

Atherosclerosis (AS), the main cause of many cardiovascular diseases (CVDs), is a progressive inflammatory disease characterized by the accumulation of lipids, fibrous elements, and calcification in the innermost layers of arteries. The result is the thickening and clogging of these vessel walls. Several cell types are directly involved in the pathological progression of AS. Among them, platelets represent the link between AS, inflammation, and thrombosis. Indeed, besides their pivotal role in hemostasis and thrombosis, platelets are key mediators of inflammation at injury sites, where they act by regulating the function of other blood and vascular cell types, including endothelial cells (ECs), leukocytes, and vascular smooth muscle cells (VSMCs). In recent years, increasing evidence has pointed to a central role of platelet-derived extracellular vesicles (P-EVs) in the modulation of AS pathogenesis. However, while the role of platelet-derived microparticles (P-MPs) has been significantly investigated in recent years, the same cannot be said for platelet-derived exosomes (P-EXOs). For this reason, this reviews aims at summarizing the isolation methods and biological characteristics of P-EXOs, and at discussing their involvement in intercellular communication in the pathogenesis of AS. Evidence showing how P-EXOs and their cargo can be used as biomarkers for AS is also presented in this review.
The Algebraic Boundary of SO(2)-Orbitopes

Let $X\subset\A^{2r}$ be a real curve embedded into an even-dimensional affine space. In the main result of this paper, we characterise when the r-th secant variety to $X$ is an irreducible component of the algebraic boundary of the convex hull of the real points $X(\R)$ of $X$. This fact is then applied to 4-dimensional SO(2)-orbitopes and to the so called Barvinok-Novik orbitopes to study when they are basic closed as semi-algebraic sets. In the case of 4-dimensional SO(2)-orbitopes, we find all irreducible components of their algebraic boundary.
Priors from Frequency-Domain Dummy Observations

By exploiting the insight that the misspecification of dynamic stochastic general equilibrium (DSGE) models is more prevalent at some frequencies than at others, we develop methods that enable different degrees of relaxation of the DSGE restrictions in different directions. We approximate the DSGE model by a vector autoregression. Dummy observations are constructed from the DSGE model and converted into the frequency domain. By re-weighting the frequency domain dummy observations we can control the extent to which the restrictions derived from economic theory are relaxed. Bayesian marginal data densities can then be used to obtain a data-driven procedure that determines the optimal degree of shrinkage toward the DSGE model restrictions. We provide several numerical illustrations of our procedure. JEL CLASSIFICATION: C32, E52, F41
On compound Poisson type limiting likelihood ratio process arising in some change-point models

Different change-point type models encountered in statistical inference for stochastic processes give rise to different limiting likelihood ratio processes. In a previous paper of one of the authors it was established that one of these likelihood ratios, which is an exponential functional of a two-sided Poisson process driven by some parameter, can be approximated (for sufficiently small values of the parameter) by another one, which is an exponential functional of a two-sided Brownian motion. In this paper we consider yet another likelihood ratio, which is the exponent of a two-sided compound Poisson process driven by some parameter. We establish, that similarly to the Poisson type one, the compound Poisson type likelihood ratio can be approximated by the Brownian type one for sufficiently small values of the parameter. We equally discuss the asymptotics for large values of the parameter.
Software engineering intelligent education platform based on LSTM training model

With the rapid development of the Internet, the way of education is gradually tending to the network platform, so the intelligent education platform plays an extremely important role in the diversified way of education. It has always been an important issue in software engineering teaching to provide learners with reading materials of appropriate complexity. At present, the automatic classification of text complexity is still mainly based on the construction of linear model formulas. But due to the limited number of features eventually entering the model, the accuracy is generally not high, and it is difficult to extend to other data sets. This paper aims to explore the performance of neural network technology in the text complexity classification models with the help of multi-dimensional features and feature optimization. After comparative experiments, the text complexity classification model based on the neural network has the best performance. Its accuracy, recall, and F1 comprehensive evaluation indicators in cross-validation are better than other methods, which not only have higher prediction accuracy, but also have more stable performance. The model established from that has a strong generalization ability for new data, with obvious advantages.
Orthogonal to Backward Mean Transformation for Dynamic Panel Data Models

The within‐groups estimator is inconsistent in dynamic panels with fixed as the individual sample mean of the lagged dependent variable used in the within transformation is contemporaneously correlated with the idiosyncratic error term. This paper suggests transforming the lagged dependent variable into orthogonal deviations from its individual backward mean, which is contemporaneously uncorrelated with the idiosyncratic error term. As this transformation eliminates the individual effects as but not for fixed, this alternative estimator is consistent for but inconsistent for and fixed. The inconsistency for fixed is shown to be negligibly small, though. Moreover, a Monte Carlo simulation shows that overall, it has superior small sample properties compared to other dynamic panel data estimators.
Quantum Computing with Variable Complex Plane. Light Beam Guide Implementation

Following the B. Hiley belief that unresolved problems of conventional quantum mechanics could be the result of a wrong mathematical structure, an alternative basic structure is suggested. Critical part of the structure is modification of the sense of commonly used terms state, observable, measurement giving them a clear unambiguous definition. This concrete definition, along with using of variable complex plane, is quite natural in geometric algebra terms. It helps to establish a feasible language for the area of quantum computing. The suggested approach is used then in the fiber optics quantum information processing scenario.
EVIDENCE FOR JET LAUNCHING CLOSE TO THE BLACK HOLE IN GRB 101219B—A FERMI GRB DOMINATED BY THERMAL EMISSION

We present observations by the Fermi Gamma-Ray Space Telescope Gamma-Ray Burst Monitor (GBM) of the nearby (z = 0.55) GRB 101219B. This burst is a long GRB, with an associated supernova and with a blackbody (BB) component detected in the early afterglow observed by the Swift X-ray Telescope (XRT). Here we show that the prompt gamma-ray emission has a BB spectrum, making this the second such burst observed by Fermi GBM. The properties of the BB, together with the redshift and our estimate of the radiative efficiency makes it possible to calculate the absolute values of the properties of the outflow. We obtain an initial Lorentz factor Γ = 138 ± 8, a photospheric radius r phot = 4.4 ± 1.9 × 10 11 ? > cm, and a launch radius r 0 = 2.7 ± 1.6 × 10 7 ? > cm. The latter value is close to the black hole and suggests that the jet has a relatively unobstructed path through the star. There is no smooth connection between the BB components seen by GBM and XRT, ruling out the scenario that the late emission is due to high-latitude effects. In the interpretation that the XRT BB is prompt emission due to late central engine activity, the jet either has to be very wide or have a clumpy structure where the emission originates from a small patch. Other explanations for this component, such as emission from a cocoon surrounding the jet, are also possible.
Decoupling Control Strategy for Bioreactor Models Designed by a Non Standard Canonical Form

Abstract The decoupling and synthesis problem for bioreactor models are investigated. A non-standard canonical form that describes the output dynamics of such systems is proposed. The new canonical form is performed by a system of non-linear differential equations exhibiting state-input and input-input interactions. This representation is selected to derive a decoupling control strategy between the influent concentrations of the various species in the reactor and the dilution rates. It is shown that a systematic design of the proposed method allows the synthesis of auxiliary control variables using different structures of non linear control techniques. It is also shown that the stability of the closed loop system can be proved using Lyapunov theory for systems without unstable internal dynamics. The applicability of the proposed design is shown in a simulation example of a bioreactor model. Keywords: Modeling, multivariable control, coupled control variables, bioreactor. 1. Introduction
Tidally driven mean flows in slowly and uniformly rotating massive main-sequence stars

We calculate tidally driven mean flows in a slowly and uniformly rotating massive main sequence star in a binary system. We treat the tidal potential due to the companion as a small perturbation to the primary star. We compute tidal responses of the primary as forced linear oscillations, as a function of the tidal forcing frequency $\omega_{\rm tide}=2(\Omega_{\rm orb}-\Omega)$, where $\Omega_{\rm orb}$ is the mean orbital angular velocity and $\Omega$ is the angular velocity of rotation of the primary star. The amplitude of the tidal responses is proportional to the parameter $f_0\propto (M_2/M)(a_{\rm orb}/R)^{-3}$, where $M$ and $M_2$ are the masses of the primary and companion stars, $R$ is the radius of the primary and $a_{\rm orb}$ is the mean orbital separation between the stars. For a given $f_0$, the amplitudes depend on $\omega_{\rm tide}$ and become large when $\omega_{\rm tide}$ is in resonance with natural frequencies of the star. Using the tidal responses, we calculate axisymmetric mean flows, assuming that the mean flows are non-oscillatory flows driven via non-linear effects of linear tidal responses. We find that the $\phi$-component of the mean flow velocity dominates. We also find that the amplitudes of the mean flows are large only in the surface layers where non-adiabatic effects are significant and that the amplitudes are confined to the equatorial regions of the star. Depending on $M_2/M$ and $a_{\rm orb}/R$, the amplitudes of mean flows at the surface become significant.
A One-dimensional Discrete Model of Biased Random Walk Relating to Bacterial Chemo-taxis

A biased one-dimensional random walk model is proposed. This model adopts biased rules that include the features of bacterial chemo-taxis. In this model, a model cell moves along a discretized number line sensing whether it has approached or receded from the origin where a chemical attractant exists. A steady probability distribution function of the cell’s existence is analytically obtained and it is confirmed by numerical simulations. The biased rules introduce advection effect into random diffusive motion: the probability distribution indicates model cells’ accumulation around the origin, which corresponds to the spatial migration of bacterial cells around a chemical attractant.
The implementation of air defense large data management application platform based on Internet of Things

Concerning the traditional GIS-based civil air defense management application system not meeting the requirements of the internet of things big data, the construction ideas and key technologies of the air defense big data management application platform based on Internet of Things (IoT) technology are studied. After analyzing big data of civil air defense, five-layer SOA architecture is proposed to build the cloud framework of topological structure. Then, the design of the database and distributed storage of images is described. Finally, aiming at the practical application requirements, four modules of real-time data acquisition, large data management, real-time statistical analysis of data, and online data display are realized. The system is of great significance to the management and application of large data information for civil air defense projects.