Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of the Polyphenols in Salicornia: From Recovery to Health-Promoting Effect

Nowadays, there has been considerable attention paid toward the recovery of waste plant matrices as possible sources of functional compounds with healthy properties. In this regard, we focus our attention on Salicornia, a halophyte plant that grows abundantly on the coasts of the Mediterranean area. Salicornia is used not only as a seasoned vegetable but also in traditional medicine for its beneficial effects in protecting against diseases such as obesity, diabetes, and cancer. In numerous research studies, Salicornia consumption has been highly suggested due to its high level of bioactive molecules, among which, polyphenols are prevalent. The antioxidant and antiradical activity of polyphenols makes Salicornia a functional food candidate with potential beneficial activities for human health. Therefore, this review provides specific and compiled information for optimizing and developing new extraction processes for the recovery of bioactive compounds from Salicornia; focusing particular attention on polyphenols and their health benefits.
Composite Higgs theory

I review the idea of realizing the Higgs as a composite pseudo-Nambu-Goldstone boson of a new strongly-interacting sector and collect major constraints on the parameter space of minimal models. Besides limits from electroweak precision tests, LHC searches for resonances and bounds due to Higgs-coupling modifications will be discussed in detail. Finally, the issue of light top partners in these models will be explored, including ways to avoid them which lead to interesting implications for flavor observables.
A Principle of Regulating the Collective Effect of Assembling Patterns in a Moderate Number of Equivalent Finite Regular Arrays of Active Nanoelements due to Local Transferring Information by Pairs Hopping under Synchronous Excitation

One elucidates specific findings of our recent computational study, arxiv.org/cond-mat/0307215 with the purpose to reveal a prospect of relevant feasibility research on regulating the process of assembling nano-sized elements into functional materials and systems. This is thought to contribute to solving the task of obtaining systems that being composed from nanoelements would have even macroscopic sizes while preserving required stability as well as designing such adaptive systems. In this respect, one explains inferences drawn from our findings that have shown that accomplishing the collective effect (CE) of assembling the active nano-elements into functional quasi two dimensional patterns over finite surfaces within a number, M of subsystems, being of the same type but different in details, may be regulated by changing the ambient conditions. This regulation of the CE requires that the immobile nano-elements, arranged into regular array over the surface, are to be activated due to local information transfer realized by hopping nano-sized elements, each represented by a pair of its ends only: sending end and the receiving one; two neighbor sites of the array identify position of that pair. Then, the CE may be regulated by varying the number M, a common forcing factor and efficiency of projecting the regularity of the array onto the CE.
Exploring the Potential of Grape Pomace Extract to Inhibit Thermo-Oxidative Degradation of Sunflower Oil: From Routine Tests to ATR-FTIR Spectroscopy

Exploring new sources of natural antioxidants is of great interest to edible oil producers, in line with the toxicological problems generated by the use of synthetic antioxidants. This study assesses the potential of lyophilized Pinot Noir grape pomace extract (GPE) to enhance the sunflower oil stability against thermo-oxidative damage compared to BHT during a prolonged exposure to convective heat at 185 °C. Oil thermo-oxidation was monitored based on specific indices such as peroxide value (PV), para-anisidine value (p-AV), inhibition of oil oxidation (IO), total oxidation (TOTOX) value, conjugated dienes and trienes (CDs, CTs), but also by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), where absorbance ratios A 3009 cm−1/A 2922 cm−1 (RI), A 3009 cm−1/A 2853 cm−1 (RII), A 3009 cm−1/A 1744 cm−1 (RIII) and RIV = A 1744 cm−1/A 2922 cm−1 (RIV) were investigated. GPE showed a significant inhibitory effect on oil thermo-oxidation and this response was concentration-dependent. Substantial decreases in the investigated indices, compared to the control without added antioxidants, were obtained after 4 h and 8 h of heat exposure of the 800 ppm GPE sample: PV (47%; 42%), p-AV (38%; 33%), IO (54%; 46%), TOTOX (41%; 37%), CDs (46%; 39%), CTs (44%; 29%). Oil exposure to heat resulted in changes in RI–RIV attributed to the reduction in the degree of unsaturation, in response to primary and secondary lipid oxidation. FTIR spectroscopy can be used to differentiate untreated and heat-treated oils based on the absorbance ratios. An inhibitory effect close to that of BHT was achieved by 500 ppm GPE, while a dose of 800 ppm provided greater protection against thermo-oxidation. Our results promote GPE as a natural additive to limit the thermo-oxidative damage of plant oils.
Impact of preprocessing methods on the Raman spectra of brain tissue.

Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.
Intelligent Traffic Data Transmission and Sharing Based on Optimal Gradient Adaptive Optimization Algorithm

This work aims to improve the transmission and sharing efficiency of intelligent transportation data and promote the further development of intelligent transportation and smart city. In this work, an EEMR (Energy Efficient Multi-hop Routing) is designed for the intelligent transportation wireless sensor network. In the EEMR algorithm, the base station runs the IAP clustering algorithm for network clustering after receiving the information of all surviving nodes. A method called ACRR (Adaptive Cluster-head Round Robin) is proposed for local dynamic election of cluster heads. In addition, the deep learning-based stochastic gradient descent algorithm and its evolution algorithm are sorted out, and its application in practical scenarios is analyzed. Due to the disadvantages of the adaptive algorithm in the current data processing process, the gradient optimization algorithm based on deep learning is adopted and the concept of adaptive friction coefficient is applied to the Adam algorithm to obtain a new adaptive algorithm (TAdam). The simulation experiment reveals that the proportion of network surviving nodes of the EEMR algorithm is still as high as 90% in 1,500 rounds of data collection. This shows that the EEMR algorithm achieves the energy balance of the network nodes as much as possible while minimizing the system energy consumption. In application scenario 1, when the network surviving nodes of the four data collection algorithms compares dropped below 40%, the number of surviving nodes in the EEMR algorithm is still as high as 97.6%. On the PTB (Penn Tree Bank) test data set, the TAdam algorithm shows the fastest convergence speed and the best generalization performance. The TAdam algorithm based on deep learning discussed in this work was of great significance for improving the transmission and sharing efficiency of intelligent transportation data.
Neural network operations and Susuki–Trotter evolution of neural network states

It was recently proposed to leverage the representational power of artificial neural networks, in particular Restricted Boltzmann Machines, in order to model complex quantum states of many-body systems [G. Carleo and M. Troyer, Science 355(6325) (2017) 602. ]. States represented in this way, called Neural Network States (NNSs), were shown to display interesting properties like the ability to efficiently capture long-range quantum correlations. However, identifying an optimal neural network representation of a given state might be challenging, and so far this problem has been addressed with stöchastic optimization techniques. In this work, we explore a different direction. We study how the action of elementary quantum operations modifies NNSs. We parametrize a family of many body quantum operations that can be directly applied to states represented by Unrestricted Boltzmann Machines, by just adding hidden nodes and updating the network parameters. We show that this parametrization contains a set of universal quantum gates, from which it follows that the state prepared by any quantum circuit can be expressed as a Neural Network State with a number of hidden nodes that grows linearly with the number of elementary operations in the circuit. This is a powerful representation theorem (which was recently obtained with different methods) but that is not directly useful, since there is no general and efficient way to extract information from this unrestricted description of quantum states. To circumvent this problem, we propose a step-wise procedure based on the projection of Unrestricted quantum states to Restricted quantum states. In turn, two approximate methods to perform this projection are discussed. In this way, we show that it is in principle possible to approximately optimize or evolve Neural Network States without relying on stochastic methods such as Variational Monte Carlo, which are computationally expensive.
Horizontal and Vertical Racial Segregation in Higher Education: Examining Trends in California Public Colleges

ABSTRACT Conceptualizing and measuring trends in segregation in higher education is difficult as both vertical and horizontal sorting is prevalent and patterns vary across racial groups. In this paper, we measure various trends in racial segregation in California for 20 years. We find significant sorting by race both between and within sectors of higher education, though the overall levels of segregation are lower in California’s colleges than they are in California’s high schools. These trends have and remained relatively stable over time. We also find important differences between groups. We see that most Latinx-White and Black-White segregation is due to students attending different schools within the same sector, while Asian-White segregation is increasingly due to students attending schools in different sectors. We also find evidence that policy and structural changes, such as opening a new campus, can affect patterns of segregation across and within sectors.
Constraints on the dark matter and dark energy interactions from weak lensing bispectrum tomography

We estimate uncertainties of cosmological parameters for phenomenological interacting dark energy models using weak lensing convergence power spectrum and bispectrum. We focus on the bispectrum tomography and examine how well the weak lensing bispectrum with tomography can constrain the interactions between dark sectors, as well as other cosmological parameters. Employing the Fisher matrix analysis, we forecast parameter uncertainties derived from weak lensing bispectra with a two-bin tomography and place upper bounds on strength of the interactions between the dark sectors. The cosmic shear will be measured from upcoming weak lensing surveys with high sensitivity, thus it enables us to use the higher order correlation functions of weak lensing to constrain the interaction between dark sectors and will potentially provide more stringent results with other observations combined.
MAGI: many-component galaxy initializer

Providing initial conditions is an essential procedure for numerical simulations of galaxies. The initial conditions for idealised individual galaxies in $N$-body simulations should resemble observed galaxies and be dynamically stable for time scales much longer than their characteristic dynamical times. However, generating a galaxy model ab initio as a system in dynamical equilibrium is a difficult task, since a galaxy contains several components, including a bulge, disc, and halo. Moreover, it is desirable that the initial-condition generator be fast and easy to use. We have now developed an initial-condition generator for galactic $N$-body simulations that satisfies these requirements. The developed generator adopts a distribution-function-based method, and it supports various kinds of density models, including custom-tabulated inputs and the presence of more than one disc. We tested the dynamical stability of systems generated by our code, representing early- and late-type galaxies, with $N=$~2,097,152 and 8,388,608 particles, respectively, and we found that the model galaxies maintain their initial distributions for at least 1~Gyr. The execution times required to generate the two models were $8.5$ and $221.7$ seconds, respectively, which is negligible compared to typical execution times for $N$-body simulations. The code is provided as open-source software and is publicly and freely available at \url{this https URL}.
Analysing Accounting Professionals’ Readiness for Digital Economy Using the Theory of Organisational Readiness for Change

The Malaysian government has launched its own initiative called MyDigital with the hope of transforming the country into a digitally-driven, high income nation and a regional leader in the digital economy. As accounting is one of the professions most affected by technological advancements, the current study seeks to examine the readiness of Malaysian accountants to embrace digitalisation using the theory of the organisational readiness to change by Weiner (2009). The study was conducted using a self-administered questionnaire, which was distributed to 384 accountants registered with Malaysia Institute of Accountants (MIA). The results revealed that change valence and task knowledge have significant positive relationship with the change commitment while the resources availability failed to prove a significant impact over change commitment. Simple regression analysis also proved that change valence, task knowledge and resources availability have a positive significant correlation towards change efficacy. The findings of the current study will provide a worthy understanding into the Malaysian accounting professionals' readiness to embrace the digital transformation, as very few in-depth studies have been conducted in this context. Besides that, the relevant authorities particularly the government, the accounting governing bodies and the businesses in general may utilize the findings with regards to the determinants of readiness amongst accounting professionals in better preparing them to become future-fit professionals.
Theory of the quantum Hall smectic phase. I. Low-energy properties of the quantum Hall smectic fixed point

We develop an effective low-energy theory of the quantum Hall (QH) smectic or stripe phase of a two-dimensional electron gas in a large magnetic field in terms of its Goldstone modes and of the charge fluctuations on eachstripe. This liquid-crystal phase corresponds to a fixed point that is explicitly demonstrated to be stable against quantum fluctuations at long wavelengths. This fixed-point theory also allows an unambiguous reconstruction of the electron operator. We find that quantum fluctuations are so severe that the electron Green function decays faster than any power law, although slower than exponentially, and that consequently there is a deep pseudo-gap in the quasiparticle spectrum. We discuss, but do not resolve, the stability of the quantum Hall smectic to crystallization. Finally, the role of Coulomb interactions and the low-temperature thermodynamics of the QH smectic state are analyzed.
Investigating the origin of cyclical wind variability in hot massive stars – II. Hydrodynamical simulations of corotating interaction regions using realistic spot parameters for the O giant ξ Persei

OB stars exhibit various types of spectral variability historically associated with wind structures, including the apparently ubiquitous discrete absorption components (DACs). These features have been proposed to be caused either by magnetic fields or non-radial pulsations. In this second paper of this series, we revisit the canonical phenomenological hydrodynamical modelling used to explain the formation of DACs by taking into account modern observations and more realistic theoretical predictions. Using constraints on putative bright spots located on the surface of the O giant $\xi$ Persei derived from high precision space-based broadband optical photometry obtained with the Microvariability and Oscillations of STars (MOST) space telescope, we generate two-dimensional hydrodynamical simulations of co-rotating interaction regions in its wind. We then compute synthetic ultraviolet (UV) resonance line profiles using Sobolev Exact Integration and compare them with historical timeseries obtained by the International Ultraviolet Explorer (IUE) to evaluate if the observed behaviour of $\xi$ Persei's DACs is reproduced. Testing three different models of spot size and strength, we find that the classical pattern of variability can be successfully reproduced for two of them: the model with the smallest spots yields absorption features that are incompatible with observations. Furthermore, we test the effect of the radial dependence of ionization levels on line driving, but cannot conclusively assess the importance of this factor. In conclusion, this study self-consistently links optical photometry and UV spectroscopy, paving the way to a better understanding of cyclical wind variability in massive stars in the context of the bright spot paradigm.
Exact solutions for one‐dimensional transient response of fluid‐saturated porous media

Based on the Biot theory, the exact solutions for one‐dimensional transient response of single layer of fluid‐saturated porous media and semi‐infinite media are developed, in which the fluid and solid particles are assumed to be compressible and the inertial, viscous and mechanical couplings are taken into account. First, the control equations in terms of the solid displacement u and a relative displacement w are expressed in matrix form. For problems of single layer under homogeneous boundary conditions, the eigen‐values and the eigen‐functions are obtained by means of the variable separation method, and the displacement vector u is put forward using the searching method. In the case of nonhomogeneous boundary conditions, the boundary conditions are first homogenized, and the displacement field is constructed basing upon the eigen‐functions. Making use of the orthogonality of eigen‐functions, a series of ordinary differential equations with respect to dimensionless time and their corresponding initial conditions are obtained. Those differential equations are solved by the state‐space method, and the series solutions for three typical nonhomogeneous boundary conditions are developed. For semi‐infinite media, the exact solutions in integral form for two kinds of nonhomogeneous boundary conditions are presented by applying the cosine and sine transforms to the basic equations. Finally, three examples are studied to illustrate the validity of the solutions, and to assess the influence of the dynamic permeability coefficient and the fluid inertia to the transient response of porous media. Copyright © 2010 John Wiley & Sons, Ltd.
Surface-integral expressions for the multipole moments of post-Newtonian sources and the boosted Schwarzschild solution

New expressions for the multipole moments of an isolated post-Newtonian source, in the form of surface integrals in the outer near-zone, are derived. As an application we compute the ‘source’ quadrupole moment of a Schwarzschild solution boosted to uniform velocity, at the third post-Newtonian (3PN) order. We show that the consideration of this boosted Schwarzschild solution (BSS) is enough to uniquely determine one of the ambiguity parameters in the recent computation of the gravitational wave generation by compact binaries at 3PN order: ζ = −7/33. We argue that this value is the only one for which the Poincaré invariance of the 3PN wave generation formalism is realized. As a check, we confirm the value of ζ by a different method, based on the far-zone expansion of the BSS at fixed retarded time, and a calculation of the relevant nonlinear multipole interactions in the external metric at the 3PN order.
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution trajectory. We perform extensive evaluations on two datasets: (i) the existing"Strogatz"dataset featuring two-dimensional systems; (ii) ODEBench, a collection of one- to four-dimensional systems that we carefully curated from the literature to provide a more holistic benchmark. ODEFormer consistently outperforms existing methods while displaying substantially improved robustness to noisy and irregularly sampled observations, as well as faster inference. We release our code, model and benchmark dataset publicly.