Miracle-dz/UniFAR-FTUs huggingface dataset:
THE EFFECT OF SUPPLEMENTING NATIVE RUMEN MICROBES ON MILK PRODUCTION OF DAIRY CATTLE By

THE EFFECT OF SUPPLEMENTING NATIVE RUMEN MICROBES ON MILK PRODUCTION OF DAIRY CATTLE By Katelyn E. Goldsmith Dairy cows are commonly fed direct-fed microbials (DFM) to improve milk production and efficiency. Most DFM are not native to the dairy cow rumen which may limit their ability to interact with the native microbiome. We evaluated the effects of two DFM supplements containing 4 native rumen microorganisms on the production of dairy cows. Ninety Holstein cows (45 ± 10 kg milk/d, mean ± standard deviation; 40% primiparous) were fed a common diet. After 14 d, they were blocked by parity, days in milk, and energy corrected milk (ECM) per unit of metabolic body weight. Within block, cows were randomly assigned to treatments, which were top-dressed daily for the next 112 d. Treatments were 150 g of ground corn mixed with 1) no live DFM (CON), 2) 5 g of a live DFM (Galaxis 2.0; G2), and 3) 5 g of a live DFM (Galaxis 2.0 Plus; G2P). G2 and G2P were products of Native Microbials Inc. (San Diego, CA) and contained the same organisms but in different concentrations. Supplementation with DFM did not alter yield of total milk, protein, or fat, but slightly decreased body weight gain and body condition score gain with no difference between G2 and G2P. DFM tended to decrease dry matter intake (DMI) but did not significantly improve feed efficiency (ECM/DMI). DFM did not alter digestibility of fiber, starch, protein, or fat, and did not alter concentrations of glucose or non-esterified fatty acids but tended to decrease concentration of insulin in plasma. DFM decreased somatic cell counts in milk with no difference between G2 and G2P. In conclusion, supplementation with native DFM had little impact on milk production and efficiency.
Analýza měřicích nástrojů mapujících zátěž formálních pečovatelů ANALYSIS OF MEASURING TOOLS MAPPING THE BURDEN OF FORMAL CAREGIVERS

of social services, espe­ cially adequate staffing (above all direct carer’s staffing) is becoming more and more important. How­ ever, this profession is often undrepaid and associated with psychic as well as physical and social work­ load. The submitted review article focuses on analysis of instruments that are used domestically and abroad to measure the level of the caregiver’s workload. Out of 32 measuring instruments, an overview of 9 fields describing the caregiver’s workload was created. Learning about the caregiver’s workload and responding to the needs they have in the above mentioned fields may prevent the burnout syn­ drome, increased morbidity, and, at the same time, it has preventive effect in various cases that may occur in this demanding job.
A Patch-Based Deep Learning Approach for Land-Classification of Sentinel-2 Satellite Imagery

The application of machine learning to the remote sensing field has produced many exciting new advancements. One of these is in land-classification: identifying the land-cover content present in satellite imagery. The use of deep neural networks has provided stateof-the-art accuracies for land-cover classification. This project experiments with deep learning architectures and novel image handling to methods to improve the classification accuracies achievable by Global Surface Intelligence, a geospatial analytics company. By implementing segmentation and patch-based training, we successfully show that by expanding their current methodology to consider spatial content, they can achieve increased classification accuracies.
The formation of interstellar jets by the convergence of supersonic conical flows

The interaction of a stellar wind with a nonuniform environment leads, under the assumption of steady state, to the formation of an ovoidal cavity with acute ending tips. The stellar wind recollected by the walls of the cavity ends up being deposited at the tips. Here, it is shown that this focusing effect leads to the formation of a narrow cylindrical stream or 'jet' of reshocked stellar wind matter moving directly away from the star with a large velocity. A 'typical' T Tauri star may produce jets with densities of 1000-10,000/cu cm, velocities about 100 km/s, widths about 0.00001-0.001 pc over a length of 0.001-0.01 pc. The opening angles are 7 deg or less.
Progressive Material Caching

The evaluation of material networks is a relatively resource-intensive process in the rendering pipeline. Modern production scenes can contain hundreds or thousands of complex materials with massive networks, so there is a great demand for an efficient way of handling material networks. In this paper, we introduce an efficient method for progressively caching the material nodes without an overhead on the rendering performance. We evaluate the material networks as usual in the rendering process. Then, the output value of part of the network is stored in a cache and can be used in the evaluation of the next materials. Using our method, we can render the scene with performance equal to or better than that of the method without caching, with a slight difference in the images rendered with caching and without it.
Processing Natural Language Argumentation

Although natural language argumentation has attracted the attention of philosophers and rhetoricians since Greek antiquity, it is only very recently that the methods and techniques of computational linguistics and machine learning have become sufficiently mature to tackle this extremely challenging topic. Argument mining, the new and rapidly growing area of natural language processing and computational models of argument, aims at automatic recognition of argument structures in large resources of natural language texts. The goal of this chapter is to familiarise the reader focused on formal aspects of argumentation with this approach, and to show how argument structures, e.g. those studied in abstract argumentation frameworks, can be extracted, providing a bridge between mathematical models and natural language. To this end, we first describe the typical argument mining pipeline and related tasks, and then present in more detail a specific example of work in this area.
Study on the Coordinated Development of Water Ports and Urban Economy in Southern Sichuan Urban Agglomeration

"Prospering the city with port, prospering the city with port, developing the city with port, and prospering together" is the goal of the coordinated development of the relationship between water transport economy and urban development. This paper studies the relationship between the development of four cities in southern Sichuan and port and waterway economy, the evaluation index system of coordination between water transport economy and urban development is established. This paper puts forward a quantitative analysis method to measure the coordinated development degree of port and city economy, to evaluate the coordinated development level of the port and city for scientific research institutions and government decision-making bodies, it provides a systematic and complete method. For promoting the new development of the economic relationship between water ports and cities, to explore the new path of the coordinated development of water transport economy and city, deepen the understanding of the development law and trend of the relationship between water transport economy and city development has strong application value. It has an important guiding role and reference significance to promote the coordinated development of China's water transport economy and cities.
Fast and spectrally accurate evaluation of gyroaverages in non-periodic gyrokinetic-Poisson simulations

We present a fast and spectrally accurate numerical scheme for the evaluation of the gyroaveraged electrostatic potential in non-periodic gyrokinetic-Poisson simulations. Our method relies on a reformulation of the gyrokinetic-Poisson system in which the gyroaverage in Poisson’s equation is computed for the compactly supported charge density instead of the non-periodic, non-compactly supported potential itself. We calculate this gyroaverage with a combination of two Fourier transforms and a Hankel transform, which has the near optimal run-time complexity $O(N_{\unicode[STIX]{x1D70C}}(P+\hat{P})\log (P+\hat{P}))$ , where $P$ is the number of spatial grid points, $\hat{P}$ the number of grid points in Fourier space and $N_{\unicode[STIX]{x1D70C}}$ the number of grid points in velocity space. We present numerical examples illustrating the performance of our code and demonstrating geometric convergence of the error.
DESIGN OPTIMIZATION OF VARIABLE STIFFNESS COMPOSITE LAMINATES USING SURROGATE MODELS FOR MINIMUM COMPLIANCE OF CURVED WING PANELS

In this study, variable stiffness curved wing panels are optimized for minimum compliance using surrogate models. With the advance of improved manufacturing capabilities, designers have the ability to design more complex shape and efficient composite structures. In the past three decades, variable stiffness (VS) composite laminates are introduced to the literature. Variable stiffness composites have definite advantages over straight fiber laminates because of the high number of design alternatives they offer. In this study, a curved wing panel is optimized for minimum compliance. To reduce the cost of the design optimization of curved VS composite laminate, a surrogate model is generated from the results of finite element analysis (FEA) in the optimization process, and optimizations are performed with the surrogate model. The reference fiber path definition is determined analytically over the curved composite wing skin panel. The three variables used in the reference fiber path definition are optimized for minimum compliance of the curved wing panel by incorporating the minimum curvature and Tsai-Wu failure constraints.
The Credit Risk Assessment for Chinese Companies Based on CAFÉ System by Using Bigdata Method

This paper examines how the CAFÉ System developed by using bigdata approach is able to resolve three problems for which the current credit rating market in China facing such as “the rating is falsely high; the differentiation of credit rating grades is insufficient; and the poor performance of p redicting early warning and related issues”. These issues are done by redefining the "BBB" as the basic investment level in accordance with international practice in capital markets, and cases study shows that how the so-called “CAFÉ Credit” is able to resolve the major issues in the capital market in China for credit rating .
A local construction of zip period maps of Shimura varieties

– Let S be the special ﬁbre of a Shimura variety of Hodge type of good reduction at a fixed place above p . We give a local approach to the construction of the zip period map for S which is used to define the Ekedahl-Oort strata of S . The method employed is p -adic and group-theoretic in nature.
Low-level Algorithms for Continual and Meta Reinforcement Learning (Proposal)

The reinforcement learning (RL) approach to artificial intelligence has had many impressive successes, including superhuman performance in Backgammon, Atari games, Go, Chess, Poker, StarCraft, and simulated race-car driving. The RL approach is special in that it treats the entirety of an autonomous intelligent agent interacting with its environment. Even simple RL agents sense their environment, take action, and have a goal (maximizing reward). More advanced agents integrate perception and planning into their decision-making.
\Carboxylated Chitosan Nanocrystals: Novel Synthetic Route and 1 Application as Superior Support for Gold-Catalyzed Reactions 2

Chitin nanocrystals (ChNCs) were prepared by partial cleavage of glycosidic bonds in 17 chitin with concurrent oxidation of chitin C6 primary alcohols to produce carboxylate 18 groups on the surface of the ChNCs. Following alkaline deacetylation of the ChNCs in the 19 presence of NaBH 4 to inhibit “end-peeling” afforded chitosan nanocrystals (ChsNCs) with 20 a degree of deacetylation (DDA) >80%. Transmission electron microscopy (TEM), X-ray 21 diffraction (XRD) and Fourier-transform Infrared (FTIR) spectroscopy were used to 22 determine the morphology and composition of these carboxylated ChNCs and ChsNCs. 23 Subsequently, two methods were used to deposit Au onto the nanocrystals, and the 24 catalytic activities of the resulting biomass-based nanocatalysts were tested for the 4- 25 nitrophenol reduction and the aldehyde-amine-alkyne (A 3 ) coupling reaction. In particular, 26 Au nanoparticles over ChsNCs featured the highest turnover frequency value for the 4- 27 nitrophenol reduction reported uncontrolled produce Two different methods were used to create Au-immobilized ChsNC catalysts in which the composition and topography of these nanomaterials can be directly altered through the reaction conditions. The catalytic properties of the nanomaterial were then investigated in the reduction of 4-nitrophenol and the A 3 coupling reaction. We have shown that nanoscale chitin and chitosan have a clear advantage in activity over bulk chitin and chitosan as support materials for heterogenous catalysis. A highly dispersed dual Au I /Au 0 nanocatalyst (Au@ChsNC-HR) fabricated by direct hydrogen reduction of HAuCl 4 on ChsNC showed significant activity for both model catalyst reactions. To the best of our knowledge, the Au@ChsNC-HR nanocatalyst exhibits the highest reported TOF for the classical 4-nitrophenol reduction reaction on carbon based-supports. From this work, we hope to show the potential of deriving high value products from chitinous waste streams 420 obtained from the seafood industry, as well as exhibit the unique physicochemical properties of ChsNCs conferred by the both different functional groups and its nanorod- structure. This work provides a prospective of ChsNCs as a new bio-nanomaterial that can compete with and possibly overcome CNCs in terms of applicability and efficacy.
Center for Extended Magnetohydrodynamic Modeling

A stripping column installation especially for separating starting materials with a high boiling point difference comprises a stripping column having a withdrawal plate and a column sump, and a circulating boiler connected with the stripping column and having a pipe bundle through which a sump product from the stripping column flows. The circulating boiler has an upper plate and a separating wall which separates a space above the upper plate into two chambers. One of the chambers has a connection for receiving the sump product from the stripping column, while the other of the chambers has a connection for discharging the sump product after heating in the boiler. The circulating boiler is located relative to the stripping column so that a height difference is produced between a liquid level on the withdrawal plate of the stripping column and the upper plate of the circulating boiler, the height difference having a value selected so that a total pressure of a liquid column at each point of a first portion of a pipe path in the circulating boiler lies above the vapor pressure of the sump product to be heated.
MRI-EEG Instance Discrimination for Brain Lesion Identification

—Precise identification of brain lesions is vital for interpreting lesion-deficit mapping of the brain and for the diagnosis and treatment of several neurological conditions. However, brain lesions remain elusive to most machine learning algorithms and they require human intervention and large annotated datasets to perform satisfactorily. In this paper we propose a self-supervised approach that uses contrastive learning to identify brain lesions using unannotated Magnetic Resonance Imaging (MRI) and Electroencephalogram (EEG) data.
Probing the Black Hole Engine with Measurements of the Relativistic X-ray Reflection Component

Over the last decades X-ray spectroscopy has proven to be a powerful tool for the estimation of black hole spin and several other key parameters in dozens of AGN and black hole X-ray binaries. In this White Paper, we discuss the observational and theoretical challenges expected in the exploration, discovery, and study of astrophysical black holes in the next decade. We focus on the case of accreting black holes and their electromagnetic signatures, with particular emphasis on the measurement of the relativistic reflection component in their X-ray spectra.
Scalable Risk-Informed Predictive Maintenance Strategy for Operating Nuclear Power Plants

: Over the years, the nuclear fleet has relied on labor-intensive, time-consuming preventive maintenance programs, driving up operation and maintenance costs to achieve high capacity factors. The primary objective of the research presented in this paper is to develop scalable technologies deployable across plant assets and the nuclear fleet in order to achieve a risk-informed predictive maintenance (PdM) strategy at commercial nuclear power plants (NPPs). We developed a well-constructed risk-informed PdM approach for an identified plant asset in this research, taking advantage of advancements in data analytics, machine learning, artificial intelligence, risk models, and visualization. The demonstration and deployment of these technologies would allow commercial NPPs to reliably transition from the current labor-intensive preventive maintenance program to a technology-driven PdM program, eliminating unnecessary operation and maintenance costs. The research and development approach presented in the paper is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear LLC. This paper presents a scalable risk-informed predictive maintenance framework with a brief discussion on scalable predictive modeling approach using the federate-transfer learning approach. The paper describes component to plant-level risk modeling based on a three-state Markov chain and its integration with circulating water system health information using the proportional hazard modeling approach. The state probabilities obtained are used to estimate the profit as part of the economic model. The paper also outlines the development of a user-centric visualization application to ensure the right information is available to the right person, in the right format, and at the right time. The research outcomes presented in this paper lay the foundation and provide a much-needed technical basis to start focusing on additional needs, such as the explainability and trustworthiness of machine-learning- and artificial-intelligence-based technologies as part of future research.
Estimation of Generalizability Coefficients: An Application of Structural Equation Modeling

The aim of this study is to provide guidance as to the interpretation of results of structural equation model of the variance components according to the generalizability theory. According to the findings, It is observed that G coefficients have been very close to each other which calculated with both LISREL and SPSS programs for both data sets. It has been observed that there is no any significant difference between G coefficients when this difference has been tested with Fisher’s Z test. In other words, the estimations of G coefficients that calculated with various programs have been found similar. Keywords: generalizability coefficiency, reliability, SEM, EduG
Oracle inequalities, aggregation and adaptation

Originally, oracle inequalities were developed as particularly efficient tools in mathematical statistics for deriving adaptation to an unknown parameter. Initially dedicated to the demonstration of statistical properties in the study of certain estimators, they can be extrapolated to the broader framework of aggregation in which they constitute the core of the definition of an optimal rate of aggregation. As such, they become not only mathematical tools but also precise finite sample results. In this thesis, several fields of application of oracle inequalities are presented. They are used first in a general aggregation framework and then in particular statistical models such as nonparametric density estimation, regression and classification. The obtained results map the variety of applications of oracle inequalities in mathematical statistics.
Mean-field magnetization relaxation in conducting ferromagnets

Collective ferromagnetic motion in a conducting medium is damped by the transfer of the magnetic moment and energy to the itinerant carriers. We present a calculation of the corresponding magnetization relaxation as a linear-response problem for the carrier dynamics in the effective exchange field of the ferromagnet. In electron systems with little intrinsic spin-orbit interaction, a uniform magnetization motion can be formally eliminated by going into the rotating frame of reference for the spin dynamics. The ferromagnetic damping in this case grows linearly with the spin-flip rate when the latter is smaller than the exchange field and is inversely proportional to the spin-flip rate in the opposite limit. These two regimes are analogous to the “spin-pumping” and the “breathing Fermi-surface” damping mechanisms, respectively. In diluted ferromagnetic semiconductors, the hole-mediated magnetization can be efficiently relaxed to the itinerant-carrier degrees of freedom due to the strong spin-orbit interacti.
Strong wave–mean-flow coupling in baroclinic acoustic streaming

The interaction of an acoustic wave with a stratified fluid can drive strong streaming flows owing to the baroclinic production of fluctuating vorticity, as recently demonstrated by Chini et al. (J. Fluid Mech., 744, 2014, pp. 329–351). In the present investigation, a set of wave/mean-flow interaction equations is derived that governs the coupled dynamics of a standing acoustic-wave mode of characteristic (small) amplitude $\unicode[STIX]{x1D716}$ and the streaming flow it drives in a thin channel with walls maintained at differing temperatures. Unlike classical Rayleigh streaming, the resulting mean flow arises at $O(\unicode[STIX]{x1D716})$ rather than at $O(\unicode[STIX]{x1D716}^{2})$ . Consequently, fully two-way coupling between the waves and the mean flow is possible: the streaming is sufficiently strong to induce $O(1)$ rearrangements of the imposed background temperature and density fields, which modifies the spatial structure and frequency of the acoustic mode on the streaming time scale. A novel Wentzel–Kramers–Brillouin–Jeffreys analysis is developed to average over the fast wave dynamics, enabling the coupled system to be integrated strictly on the slow time scale of the streaming flow. Analytical solutions of the reduced system are derived for weak wave forcing and are shown to reproduce results from prior direct numerical simulations (DNS) of the compressible Navier–Stokes and heat equations with remarkable accuracy. Moreover, numerical simulations of the reduced system are performed in the regime of strong wave/mean-flow coupling for a fraction of the computational cost of the corresponding DNS. These simulations shed light on the potential for baroclinic acoustic streaming to be used as an effective means to enhance heat transfer.