Miracle-dz/UniFAR-FTUs huggingface dataset:
Cold-Pressed Oilseed Cakes as Alternative and Sustainable Feed Ingredients: A Review

Due to the increasing demand for alternative protein feed ingredients, the utilization of oilseed by-products in animal nutrition has been sought as a promising solution to ensure cheap and environmentally sustainable feedstuffs. This review aimed to summarize the nutritional value of six cold-pressed cakes (rapeseed, hempseed, linseed, sunflower seed, camelina seed, and pumpkin seed) and the effects of their inclusion in diet for ruminant, pig, and poultry on nutrient digestibility, growth and productive performance, and quality of the products. The presented results indicated that these unconventional feed ingredients are a good protein and lipid source and have a balanced amino acid and fatty acid profile. However, contradictory results of animal production performances can be found in the literature depending on the cake type and chemical composition, dietary inclusion level, animal category, and trial duration. Due to the substantial amount of essential fatty acid, these cakes can be efficiently used in the production of animal products rich in n-3 and n-6 polyunsaturated fatty acids. However, the utilization of cakes in pig and poultry nutrition is limited because of the presence of antinutritive factors that can deteriorate feed intake and nutrient utilization.
The Effect of Social Capital on Outcomes of Global Citizenship among Taiwanese Young Adults: The Mediating Role of Political Self-efficacy

The purpose of this article is to examine the level of social capital and global citizenship in Taiwan. In this study, we argue that high social capital and political self-efficacy promote the outcomes of global citizenship. We review the development of global citizenship education policy and its association with social capital and political self-efficacy. Based on the nationwide survey dataset collected from Taiwanese universitiy students, we carefully examine the relations between social capital, political self-efficacy, and global citizenship. We adopt a multiple mediation analysis with a bootstrapped method to validate our conceptual research model. The results show that there are significant positive relationships between the nine observed variables (i.e., social trust, social proactivity, political self-efficacy and six global citizenship outcomes). In addition, our findings of mediation analysis reveal that social capital, indicated by social trust and social proactivity, indirectly affects the five outcomes of global citizenship through political self-efficacy, except for the outcome of global environmental sustainability. In light of the empirical results, this article also discusses the implications of global citizenship education policy and the formation of sustainable global citizenship.
Temperature characteristics of ZnO-based thin film bulk acoustic wave resonators

In the past few years, thin film bulk acoustic wave (BAW) devices have caught the attention of many RF researchers and developers primarily for their potential utilization in wireless applications. Some of the major advantages BAW devices present over other filter technologies in use today include size reduction and the possibility of on-chip integration. As the technology matures, it becomes necessary to more fully characterize the performance of the devices and to develop more accurate models describing their behavior. In this investigation, the effects that temperature variations have on 1.8 GHz zinc oxide (ZnO)-based solidly mounted BAW resonators (SMRs) are studied. The average temperature coefficients of the series and parallel resonant frequencies of the fabricated devices were found to be -30.5 ppm/1/spl deg/C and -32.5 ppm//spl deg/C, respectively. The slight decrease in separation between the two resonant frequencies with temperature implies there is slightly less effective coupling with increased temperature. With little temperature coefficient data for thin film ZnO available in the literature, the importance of an accurate model is evident. The resonator device performance was simulated using Ballato's electronic circuit model for acoustic devices on a SPICE-based platform. By virtue of the comparison between the predicted and measured device response, various material parameters were extracted.
Unveiling IL-33/ST2 Pathway Unbalance in Cardiac Remodeling Due to Obesity in Zucker Fatty Rats

Obesity is an epidemic condition linked to cardiovascular disease severity and mortality. Fat localization and type represent cardiovascular risk estimators. Importantly, visceral fat secretes adipokines known to promote low-grade inflammation that, in turn, modulate its secretome and cardiac metabolism. In this regard, IL-33 regulates the functions of various immune cells through ST2 binding and—following its role as an immune sensor to infection and stress—is involved in the pro-fibrotic remodeling of the myocardium. Here we further investigated the IL-33/ST2 effects on cardiac remodeling in obesity, focusing on molecular pathways linking adipose-derived IL-33 to the development of fibrosis or hypertrophy. We analyzed the Zucker Fatty rat model, and we developed in vitro models to mimic the adipose and myocardial relationship. We demonstrated a dysregulation of IL-33/ST2 signaling in both adipose and cardiac tissue, where they affected Epac proteins and myocardial gene expression, linked to pro-fibrotic signatures. In Zucker rats, pro-fibrotic effects were counteracted by ghrelin-induced IL-33 secretion, whose release influenced transcription factor expression and ST2 isoforms balance regulation. Finally, the effect of IL-33 signaling is dependent on several factors, such as cell types’ origin and the balancing of ST2 isoforms. Noteworthy, it is reasonable to state that considering IL-33 to have a unique protective role should be considered over-simplistic.
Saikosaponin A and Its Epimers Alleviate LPS-Induced Acute Lung Injury in Mice

The purpose of this work was to illustrate the effect of processing with vinegar on saikosaponins of Bupleurum chinense DC. (BC) and the protective effects of saikosaponin A (SSA), saikosaponin b1 (SSb1), saikosaponin b2 (SSb2), and saikosaponin D (SSD) in lipopolysaccharide (LPS)-induced acute lung injury (ALI) mice. We comprehensively evaluated the anti-inflammatory effects and potential mechanisms of SSA, SSb1, SSb2, and SSD through an LPS-induced ALI model using intratracheal injection. The results showed that SSA, SSb1, SSb2, and SSD significantly decreased pulmonary edema; reduced the levels of IL-6, TNF-α, and IL-1β in serum and lung tissues; alleviated pulmonary pathological damage; and decreased the levels of the IL-6, TNF-α, and IL-1β genes and the expression of NF-κB/TLR4-related proteins. Interestingly, they were similar in structure, but SSb2 had a better anti-inflammatory effect at the same dose, according to a principal component analysis. These findings indicated that it may not have been comprehensive to only use SSA and SSD as indicators to evaluate the quality of BC, especially as the contents of SSb1 and SSb2 in vinegar-processed BC were significantly increased.
Discovery of a Nova in the Virgo Galaxy M100

We present the V and I light curves of a nova discovered in the disk of the spiral galaxy M100, located in the Virgo Cluster. In spite of the fact that the light curve is not well sampled around maximum light and the reddening to the nova is not accurately known, by adopting the maximum magnitude versus rate of decline relation by Della Valle & Livio we derive a distance modulus to M100 of μ0 ~31.0 ± 0.3 mag, fully consistent with the Cepheid distance modulus of 31.04 ± 0.17 mag found by Ferrarese et al. from the same set of HST/WFPC2 data.
On vortex formation from a cylinder. Part 1. The initial instability

Vortex shedding from a circular cylinder is examined over a tenfold range of Reynolds number, 440 ≤ Re ≤ 5040. The shear layer separating from the cylinder shows, to varying degrees, an exponential variation of fluctuating kinetic energy with distance downstream of the cylinder. The characteristics of this unsteady shear layer are interpreted within the context of an absolute instability of the near wake. At the trailing-end of the cylinder, the fluctuation amplitude of the instability correlates well with previously measured values of mean base pressure. Moreover, this amplitude follows the visualized vortex formation length as Reynolds number varies. There is a drastic decrease in this near-wake fluctuation amplitude in the lower range of Reynolds number and a rapid increase at higher Reynolds number. These trends are addressed relative to the present, as well as previous, observations.
Yee-like schemes on staggered cellular grids: a synthesis between FIT and FEM approaches

We propose an analysis (discretization techniques, convergence) of numerical schemes for Maxwell equations which use two meshes (not necessarily tetrahedral), dual to each other. Schemes of this class generalize Yee's "finite difference in time domain" method (FDTD). We distinguish network equations (the discrete equivalents of Faraday's law and Ampere's relation) which can be set up without any recourse to finite elements, and network constitutive laws, whose validity cannot be assessed without them. This establishes a complementarity between "finite integration techniques" (FIT) and the finite element method (FEM). As an example, a Yee-like method on a simplicial mesh and its so-called "orthogonal" dual, is described, and its convergence is proved.
Crossover designs in the presence of carry‐over effects from two factors

Experiments, used in the telecommunications industry and elsewhere, are considered that involve the simultaneous application of levels of two unrelated factors, treatments and stimuli, to each of several subjects in a succession of time periods. The existence is suspected of carry‐over effects of treatments and stimuli, in the period immediately following the period of their application. Methods are given for the construction of separate sequences of treatments and of stimuli; these methods are based on the Latin squares of Williams and of Russell. In the resulting designs, the treatments and stimuli are either orthogonal or nearly orthogonal, and the coincidence of the direct and carry‐over effects of each factor is either balanced or nearly balanced. The efficiencies of the designs are assessed by comparing the average variances of elementary contrasts in the levels of each factor with appropriate lower bounds.
A Legendre collocation method for fractional integro-differential equations

A numerical method for solving the linear and non-linear fractional integro-differential equations of Volterra type is presented. The fractional derivative is described in the Caputo sense. The method is based upon Legendre approximations. The properties of Legendre polynomials together with the Gaussian integration method are utilized to reduce the fractional integro-differential equations to the solution of algebraic equations. Illustrative examples are included to demonstrate the validity and applicability of the presented technique and a comparison is made with existing results.
Boom potential of a rotating satellite in sunlight

An interpretation is provided for the behavior of long boom potential measurements taken on the spinning P78-2 (SCATHA) satellite at near geosynchronous altitudes. This study uses data taken during a quiet day, with the satellite in sunlight. The data show periodic variations with a maximum amplitude of 6 V. The theory explains why the variations correlate well with sun direction but not with the geomagnetic field. A current balance model, assuming a Maxwellian distribution of photoelectrons, is studied. The photoelectron temperature, the degrees of positive charging of the boom and of the satellite, and the ambient electron flux are calculated. Deviations from the model are discussed.
Variable Bandwidth Kernel Estimators of Regression Curves

On montre qu'en termes de l'erreur en moyenne quadratique integree asymptotique, les estimateurs du noyau avec un certain choix de largeur de bande local sont superieurs aux estimateurs ordinaires du noyau avec un choix de largeur de bande global, si on utilise des largeurs de bandes optimales
The set of geometrically infinitely divisible distributions

Klebanove.a. (1984) have shown that the set of geometrically infinitely divisible distributions coincides with the closure of the set of compound geometric distributions. We prove that from this it follows that (mixtures of) log-convex densities on (0,00) are geometrically infinitely divisible. The results of Pillai and Sandhya (1990) are easy consequences of this.
The discrete geometric conservation law and the nonlinear stability of ALE schemes for the solution of flow problems on moving grids

Abstract Discrete geometric conservation laws (DGCLs) govern the geometric parameters of numerical schemes designed for the solution of unsteady flow problems on moving grids. A DGCL requires that these geometric parameters, which include among others grid positions and velocities, be computed so that the corresponding numerical scheme reproduces exactly a constant solution. Sometimes, this requirement affects the intrinsic design of an arbitrary Lagrangian Eulerian (ALE) solution method. In this paper, we show for sample ALE schemes that satisfying the corresponding DGCL is a necessary and sufficient condition for a numerical scheme to preserve the nonlinear stability of its fixed grid counterpart. We also highlight the impact of this theoretical result on practical applications of computational fluid dynamics.
A Method of Estimating and Comparing Volumes Under Receiver Operating Characteristic (ROC) Surfaces

Receiver operating characteristic (ROC) analysis has been widely used to evaluate the performance of diagnostic tests with dichotomous outcomes. Recently the ROC methodology has been extended to three- and multiple-group classification problems. The methods typically involve construction of an ROC surface and calculation of the volume under the surface (VUS). The volume, which measures the discriminatory accuracy of a test, can be used for statistical inference. While theory and computational methods of the ROC surface analysis have been well studied and developed for some multi-class problems, very little research has been done on a classification problem commonly seen in DNA amplification assays that use an internal control. In this article, we propose using the maximum likelihood estimator (MLE) to estimate the VUS for the aforementioned diagnostic problem. We show that the MLE is a consistent estimator, having an asymptotic normal distribution. A numerical method based on Monte Carlo simulation is proposed for computing the MLE. The performance of the MLE is also evaluated using simulations. The theoretical findings aided with the numerical tool allows us to effectively evaluate diagnostic tests. The use of the method is illustrated through an example.
Interference Filters and Fabry-Perot Interferometers for the Far Infrared

This paper describes measurements on transmission interference filters for the far infrared (100–800 μ region). The reflection layers of these filters are metallic mesh. The measured coefficients of finesse, ten to sixty, are comparable with the values which can be obtained for interference filters in other spectral regions. Peak-transmission values up to 0.9 were reached. From these measurements the absorptivity of the single grids has been determined. Measurements on a variable spacing interference filter showed that the finesse is proportional to the square of the wavelength in accordance with theoretical predictions for interference filters constructed with reflection layers of one-dimensional wire gratings.
Light-Curve Diagnosis of a Hot Spot for Accretion-Disk Models

Light curves of a hot spot rotating in a relativistic Keplerian disk were found to be periodic with typically two peaks, originating from a gravitational focusing effect and a Doppler boost. On the other hand, we found that light curves of a hot spot spirally infalling in a sub-Keplerian advective disk are aperiodic with typically a single peak, originating from a gravitational focusing effect or a Doppler boost. Such a difference in the light curves of a hot spot can discriminate background disk models.
Artiﬁcial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

: The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly re-inforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as “message-passing paradigms”, “spatial-symmetry-preserving networks”, “hybrid de novo design”, and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.
Development of two-color focal-plane arrays based on HDVIP (Invited Paper)

An overview of DRS' development of a very unique two-color infrared photodiode architecture for two-color infrared focal-plane arrays based on Hg1-xCdxTe is given. One unique feature of this architecture is that it requires only a mono-color based on Hg1-xCdxTe material growth technique for its implementation. Some data from this development are presented.
The Poisson Boundary of Teichmüller Space

Abstract For a Markov operator on Teichmuller space commuting with the action of the mapping class group we prove convergence of sample paths of the associated Markov chain in the Thurston compactification and show that the Poisson boundary of the Markov operator can be identified with the space of projective measured foliations. The approach consists in using the authors' results on the Poisson boundary of the mapping class group in combination with a discretization procedure based on a Harnack inequality for Markov operators on Teichmuller space.
Recurrent-Neural-Network-Based Fractional Order Sliding Mode Control for Harmonic Suppression of Power Grid

A continuous fractional order sliding mode controller based on a developed output feedback feature selection neural network (OFFSNN) for an active power filter (APF) is studied in this article to effectively compensate grid harmonic current and improve power quality. A fractional order sliding mode manifold is introduced first. Then, a continuous fractional order sliding mode controller is adopted to resolve the shortcoming of chattering phenomenon in the conventional one by designing a continuous control law. Furthermore, considering the unknown part of APF system, a new neural structure called OFFSNN is established to estimate the unknown dynamic characteristic with high precision and low computational burden. Compared with general neural network, the proposed OFFSNN is adept at adjusting neural structure and parameters by selecting beneficial nodes as well as deleting useless ones. To demonstrate the effectiveness and superiority of the presented scheme, experiments in various cases on the APF platform are given.
Graph Diffusion Convolutional Network for Skeleton Based Semantic Recognition of Two-Person Actions

Graph Convolutional Networks (GCNs) have successfully boosted skeleton-based human action recognition. However, existing GCN-based methods mostly cast the problem as separated person's action recognition while ignoring the interaction between the action initiator and the action responder, especially for the fundamental two-person interactive action recognition. It is still challenging to effectively take into account the intrinsic local-global clues of the two-person activity. Additionally, message passing in GCN depends on adjacency matrix, but skeleton-based human action recognition methods tend to calculate the adjacency matrix with the fixed natural skeleton connectivity. It means that messages can only travel along a fixed path at different layers of the network or in different actions, which greatly reduces the flexibility of the network. To this end, we propose a novel graph diffusion convolutional network for skeleton based semantic recognition of two-person actions by embedding the graph diffusion into GCNs. At technical fronts, we dynamically construct the adjacency matrix based on practical action information, so that we can guide the message propagation in a more meaningful way. Simultaneously, we introduce the frame importance calculation module to conduct dynamic convolution, so that we can avoid the negative effect caused by the traditional convolution, wherein the shared weights may fail to capture key frames or be affected by noisy frames. Besides, we comprehensively leverage the multidimensional features related to joints’ local visual appearances, global spatial relationship and temporal coherency, and for different features, different metrics are designed to measure the similarity underlying the corresponding real physical law of the motions. Moreover, extensive experiments and comprehensive evaluations on four public large-scale datasets (NTU-RGB+D 60, NTU-RGB+D 120, Kinetics-Skeleton 400, and SBU-Interaction) demonstrate that our method outperforms the state-of-the-art methods.