Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of the Tongyang Huoxue Recipe (TYHX) on the Ito/IKur in Ischemia/Reperfusion Sinoatrial Node Cells

Background The transient outward potassium current (Ito) and the ultrarapid delayed rectifier potassium current (IKur) are major potassium currents involved in the repolarization process of sinoatrial node cells (SNCs). Methods and Results The SNCs of neonatal rats were divided into control, ischemia/reperfusion (I/R), I/R+blank serum, and Tongyang Huoxue recipe (TYHX) serum groups. Ito and IKur were recorded using the whole cell patch-clamp technique, and the current-voltage (I-V), steady-state activation (SSA), steady-state inactivation (SSI), and recovery from inactivation (RFI) curves were plotted, respectively. Compared to the control group, both the peak current density and the current density at the voltage of Ito and IKur decreased obviously in SNCs after simulated I/R, the SSA curves moved right, and the SSI curves moved left. After TYHX was added to the extracellular solution of SNCs, both the peak current density and the current density at the voltage of Ito and IKur increased significantly, the SSA curves moved left, and the SSI curves moved right with a significant difference of V1/2. The recovery from the IKur RFI curves was slightly restored, and the Ito curves did not change. Conclusions TYHX increases the peak current density, accelerates the activation, and decreases the inactivation of the Ito and IKur. This may be the mechanism of TYHX in shortening the action potential duration of repolarization, which accelerates spontaneous pulsation.
COMPARISON OF MEASURED AND PREDICTED BULK MODULI OF TANTALUM AND TUNGSTEN AT HIGH TEMPERATURES

The elastic constants of tantalum have been determined from room temperature to 500°C by means of a dynamic resonance method to obtain the adiabatic bulk modulus at elevated temperature. The adiabatic bulk modulus (Bs) of tantalum is found to be 1897 kbar at room temperature and it decreases almost linearly with a slope (dBs/dT) of −0.11 kbar/°C as temperature rises. The calculation of the Gruneisen constant γ and a parameter δ, defined as − (dBs/dT)/αBs, from the present data shows that both parameters are almost independent of the temperature (1.57 for γ and 3.1 for δ). The constancy of γ and δ at high temperatures has also been tested for tungsten by using the recent data of Lowrie and Gonas [J. Appl. Phys. 36, 2189 (1965)]. The results indicate that both γ and δ are virtually constant at high temperature. The applicability of the following equation to estimate the bulk‐modulus‐temperature relationship for the refractory metals Ta and W has also been tested: BsT=Bs25−(δγ/V)(HT−H25),where H is the entha.
Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors

Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation forecasts in wind farms. In addition, extensive quantitative and qualitative analyses have been conducted to assess the magnitude of forecasting error depending on selected factors (such as forecasting horizon, wind farm size, and a class of the forecasting method). Based on these analyses and a review of more than one hundred papers, a unique set of recommendations on the preferred content of papers addressing wind farm generation forecasts has been developed. These recommendations would make it possible to conduct very precise benchmarking meta-analyses of forecasting studies described in research papers and to develop valuable general conclusions concerning the analyzed phenomena.
Phenotypic Diversity within Ugandan Yam (Dioscorea species) Germplasm Collection

A proper understanding of the diversity of the available germplasm is an initial step for the genetic improvement of a crop through breeding. However, there is limited information on the diversity of Uganda’s yam germplasm. The study sought to characterize the diversity of yam germplasm utilized for decades in Uganda together with germplasm recently introduced from West Africa using phenotypic traits. A germplasm collection of 291 genotypes was characterized using 28 phenotypic traits. Data were subjected to multivariate analysis using principal component analysis and cluster analysis. The traits assessed were informative and discriminating, with 62% of the total variation explained among the first six principal components. Results showed that the important phenotypic traits contributing to most of the variability among the genotypes were leaves, flowering, and tuber traits. Ugandan genotypes were identified with amorphous tuber shapes compared to West African genotypes. The study has shown that there is ample phenotypic variability within the major yam genotypes in Uganda yam germplasm that can be used for genetic improvement. More in-depth molecular and biochemical studies to further understand the diversity are recommended. The preprint was made available by research square in the following link: “https://www.researchsquare. com/article/rs-1518551/v1. ”
Parallel neural network combined with sliding mode control in overhead crane control system

A novel control for a nonlinear two-dimensional (2-D) overhead crane is proposed. Instead of the complex design procedures used in classic methods, the proposed scheme combines the principles of neural networks (NNs) and variable structure systems (VSS) to derive control signals needed to drive the cart smoothly, rapidly and with limited payload swing. The merits include the robustness and model-free properties of the sliding mode and neural based controllers, respectively. Simulations performed using a scaled 2-D mathematical model of the crane confirm the effectiveness of the proposed method.
Plasma compressibility induced toroidal Alfvén eigenmode

For circular tokamaks, it is demonstrated, both analytically and numerically, that there exists a new frequency gap within the shear Alfven continuous spectrum around the Alfven frequency, ωA=vA/qR, due to the ion compressibility effect. Here, vA denotes the Alfven speed, q is the safety factor, and R the major radius. The frequency gap width, meanwhile, is proportional to the core ion beta, βi (ratio between core ion and magnetic pressures), and, correspondingly, the new discrete toroidal Alfven eigenmode (TAE) inside the gap is referred to as βTAE. Collective excitations of the βTAE by energetic ions are also analyzed.
Molecular epidemiology of rotavirus A in Iranian broiler flocks

Avian rotaviruses (RV) are still largely undefined despite being widespread in several avian species and significant economic the impact of rotavirus enteritis in poultry flocks. In this study, the presence of avian RV groups was investigated in 36 commercial poultry flocks with a history of enteric diseases in Iran. Intestinal contents of broiler chickens with diarrhea and stunting syndrome were analyzed by reverse transcription-polymerase chain reaction specific for RVs based on NSP4 gene. Through partial sequencing and BLAST analyses of 11 positive specimens, we identified avian-like RV group A (RVA) strain. There was high prevalence of group A rotaviruses (approximately 30%) in our samples. The phylogenetic analysis also revealed a close genetic relationship between the current isolates and other avian RVAs but located in the separate cluster. This study provides novel data on the prevalence of genetically different avian RVs in Iranian poultry flocks.
The Effects of Dislocation Dipoles on the Failure Strength of Wrinkled Graphene from Atomistic Simulation

This research paper studies the fracture and mechanical properties of rippled graphene containing dislocation dipoles. The atomistic simulation is performed to study the deformation behavior of pristine and defective wrinkled graphene. Graphene wrinkling considerably decreases the ultimate tensile strength of graphene with and without defects but increases the fracture strain. For graphene with the dislocation dipoles, temperature increase slightly affects mechanical properties, in contrast to graphene and graphene with Stone–Wales defect. The extremely similar slopes of the stress-strain curves for graphene with the dislocation dipoles with different arms imply that the distance between dislocations in the dipole does not have noticeable effects on the elastic modulus and strength of graphene. Defects in graphene can also affect its wrinkling; for example, preventing wrinkle formation.
Adaptive image interpolation based on local activity levels

In this paper, an adaptive warped distance method is suggested for image interpolation. This method depends on modifying the warped distance technique for image interpolation taking into consideration the level of activity in local regions of the image. This is performed by weighting the pixels used in the interpolation process with different adaptive weights. The adaptation can be extended to different traditional interpolation techniques such as bilinear, bicubic and cubic spline techniques as well as to the warped distance technique. Results show that the adaptive weighting of pixels in interpolation gives better results than that obtained using traditional interpolation methods only or by using the warped distance technique.
FTIR characterization of atmospheric fluctuations along slant paths

Sensor system noise needs to be characterized to determine the limits of detecting a feature from an observed source. For passive infrared spectral sensors, the noise is characterized in terms of the noise equivalent spectral radiance, NESR. The total NESR (NESRtotal) has two components, the internal NESR of the instrument (NESRinstr) and the external NESR of the path being viewed by the sensor (NESRpath). In the case of an FTIR instrument, the NESRinstr is measured by viewing a stable blackbody at close range thereby removing the effects of the path on the spectrally dependent noise. The standard deviation of the sine transform of the interferogram is then computed to estimate NESRinstr. In our application, however, the NESRpath is our signal, and it is measured by viewing an atmospheric scene and removing the effect due to the instrument. A histogram of the spectrally dependent noise spectrum is then computed. The full-width of this histogram is taken at the 1/e2 points and is driven by temperature and species concentration fluctuations along the path. Both of these effects can dominate over the instrument noise. In the following, we compare preliminary values of path spectral fluctuations determined from a ground-based FTIR for a selected slant path to measured values of the refractive index structure constant (Cn2) along the same path.
Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts

Background: Modern prodromal Alzheimer’s disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed. Objective: Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations. Methods: Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as on the unrelated validation cohort. Results: The algorithms achieved a performance of AUC 0.73 and AUC 0.77 in the respective training cohorts and AUC 0.81 in the unseen validation cohorts. Conclusion: The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.
Causal hidden variable model of pathogenic contamination from pig to pork

Risk assessments relating to food safety over more than one step along a production chain are frequently hampered by lack of detailed quantitative data. This study set out to develop a Bayesian hidden variable model to integrate available limited data of the combined occurrence of three bacterial pathogens, Listeria monocytogenes, Yersinia enterocolitica and Yersinia pseudotuberculosis, with causal assumptions along three steps of pork production chain. The pathogen occurrence data were animal specific both on conventional and organic pig farms and at the abattoir, but merely farm specific at meat cutting plants. The model was able to incorporate all data concerning different types of testing at different steps of the chain, and missing data values were dealt with in a straightforward manner. It provides a tool for quantitative risk assessments and for estimating the causal risk mitigation effects by combining external data with the specific follow-up data. Intervention effects are provided with Bayesian credible intervals indicating the uncertainty due to all information sources included in the model. Combined prevalence in Finnish pork was estimated to be 1–11% and it could be reduced to 0–2% if head was removed intact and rectum sealed off.
Subrelativistic Jets from Black Hole Accretion Vortices. II. The Broad Absorption Lines in Quasars

This article is an analysis of the broad absorption line properties of a theory of quasars described in a companion paper (Paper I) that differentiates radio-loud quasars from radio-quiet quasars by the magnetic field structure, or lack thereof, in the vortex of an accretion flow around a supermassive rapidly rotating black hole. Nonmagnetic vortices (the radio-quiet case) can initiate radiation pressure-driven jets, as shown in Paper I and these bipolar outflows are candidate broad absorption line regions. The primary emphases of this article are the far-field dynamics and the observational implications of the radiation-driven jets from accretion vortices conjectured to exist in radio-quiet quasars. The well-known confinement problem of the broad line absorbing gas is solved by these hypersonic underexpanded jets as they are collimated kinematically with an opening angle of the order of the inverse of the Mach number. The asymptotic jet velocities are found by a detailed analysis of the force of gravity, continuum radiation pressure, and line-driving forces on the jet plasma. The dynamics are explored through time stationary solutions. However, the general solution is considered to be a superposition of episodic quasistationary funnel ejecta as the accretion flow is most likely dynamically unsteady. The final state outflow is a series of geometrically thin clouds with a distribution of velocities ranging from 0 to ≈ 35,000 km s-1. The most significant implication of this model is that radio-loud quasars (magnetic flux filled accretion vortices) do not have broad absorption line outflows.
The Role of Structural Equation Modeling in Scale Development

Across a variety of disciplines and areas of inquiry, reliable and valid measures are a cornerstone of quality research. This is the case because to have confidence in the findings of our studies, we must first have confidence in the quality of our measures. This article briefly reviews the literature on scale development and provides an empirical demonstration of the scale development process. The example considered is the development and validation of a condom influence strategy questionnaire-short form (CISQ-S), a scale to measure ways individuals persuade their partners to use condoms. A special focus is put on the unique contribution that structural equation modeling techniques, particularly confirmatory factor analysis, bring to scale development. Latent variable modeling and its applications to scale development are also considered. Suggestions and implications for scale developers are discussed.
Fast logic and belief-bias: it’s less how smart you are than how you think

ABSTRACT The Dual strategy model of reasoning, which distinguishes between Counterexample and Statistical strategies, predicts performance on a variety of forms of reasoning and judgment. At least part of the distinction between reasoning strategies has been shown to be attentional, suggesting that strategy use should be a strong predictor of the ability to reason logically under severe time constraint. Recent results have provided evidence that strategy use is a better predictor than IQ of the ability to make logically valid inferences with belief-biased syllogisms under severe time constraint. The following study extended this result to include other measures that correlate with reasoning ability, IQ, Cognitive Reflection Test and Acceptance of Open-minded Thinking. Results show that when given a very short time (5s), strategy use along with measures of CRT are strong predictors of logical reasoning, while IQ is not.
Hybrid photovoltaic cells based on ZnO/Sb2S3/P3HT heterojunctions

We demonstrate hybrid photovoltaic (PV) cells based on n–i–p heterojunctions with incorporated zinc oxide (ZnO) films using a sol–gel method. The cells, employing stibnite (Sb2S3), and poly(3‐hexylthiophene) (P3HT) materials, as light absorption layers, constitute an active region in the indium tin oxide (ITO)/ZnO(n)/Sb2S3(i)/P3HT(p)/Ag hybrid structure. Our investigation shows that annealing temperature has a significant effect on both the crystallinity and optical absorption of Sb2S3 films. The near‐intrinsic Sb2S3 films annealed at 300 °C exhibits dark conductivity of 1.42 × 10−7 S cm−1. The performance of PV cells strongly depends on thermal treatment of Sb2S3, and on the thickness of both the Sb2S3 and P3HT layers. Electronic structures of annealed Sb2S3 films was further studied by photoelectron spectroscopy to understand the physics behind.
Racial Concordance and the Quality of Medical Care: Evidence from the Military

One explanation for insufficient use of primary care in the U.S. is a lack of trust between patients and providers – particularly along racial lines. We assess the role of racial concordance between patients and medical providers in driving use of preventive care and the implications for patient outcomes. We use unique data from the Military Health System, where we observe providers as patients so that we can identify their race, and where moves across bases change exposure to provider race. We consider patients with four chronic, deadly, but ultimately manageable illnesses, where the relationship with the provider may have the most direct and important impact on health. We find striking evidence that racial concordance leads to improved maintenance of preventive care – and ultimately lower patient mortality. Pooling across these diseases, we estimate that a one-standard deviation increase in the share of providers who are black leads to a 15% relative decline in Black mortality among those with these manageable illnesses. Our results further suggest that between 55 and 69% of this mortality impact arises through improved medication use and adherence, with other aspects of the provider-patient relationship accounting for the residual.
Effect of culture media and auxin on growth and glucosinolate accumulation in the hairy root cultures of mustard (Brassica juncea)

Brassica juncea is a vegatable that are rich in glucosinolate (GSL) content. The hairy root (HR) cultures system is one of the most useful tools for secondary metabolites (SM) biosynthesis under various growth conditions. In the past, GSLs were mostly used as biopesticides in agriculture, anti-nutritional factors in fodder, and flavors in condiments. However, in recent days, GLSs have received much attention in human health. To investigate the growth response and variation of GSLs accumulation, HRs of mustard were grown in different growth media and auxins. The HRs growth pattern varied largely under the treatments of growth media and auxin. The full-strength SH media responded greatly for achieving the highest dry weight (DW) followed by the ½ SH media and the lowest DW was obtained in full-strength MS media. In all the auxin treatments the HRs production was higher than that of the control. It was noted that at higher NAA and IBA concentrations HR production was increased than that at the lower concentrations. In addition, different growth mediums significantly influenced the GSLs accumulation in mustard HR. The results revealed that ½ B5 media showed the highest total GSLs content followed by B5 and ½ SH. Treatment of mustard HRs with auxins such as IAA and IBA negatively influenced the accumulation of GSLs except for 4-methoxyglucobrassicin. We, therefore, suggest that HRs are a viable option for improving the GSLs content from the HR culture of mustard and that SH and ½ B5 medium provides an alternative approach for mass production of HRs and GSLs in mustard, respectively.
LSTM-MPC: A Deep Learning Based Predictive Control Method for Multimode Process Control

Modern industrial processes often operate under different modes, which brings challenges to model predictive control (MPC). Recently, most MPC related methods would establish prediction models independently for different modes, which results in their control effect highly relying on switching strategies. Inspired by the powerful representation capabilities of deep learning, this article proposed a deep learning based MPC method. Specifically, the LSTM network is applied to predict behaviors of controlled system, which can automatically match different operation modes without switching strategy. Then combined with MPC framework, an adaptive gradient descent method is introduced to handle optimization problem and its constraints. In addition, stability and feasibility analysis have been conducted from the aspect of theory to ensure practical application of the proposed method. Experiments on a numerical simulation process and an industrial process platform show the strength and reliability of the proposed method, which reduces the overshoot by about 10$\%$ compared to common learning-based MPC methods and improves the control accuracy effectively.
Atomic structure of intermetallic compound Nb5Si3 by new cluster transformation analysis method

The structure of Nb5Si3 at the atomic level is fundamental for identifying its complicated structure in atomic simulations and for further understanding the phase selection behaviors during the solidification of Nb-Si alloys. In this study, the structure of Nb5Si3 was investigated using deep-learning molecular dynamic simulations. The ideal βNb5Si3 is characterized by Nb-centered Voronoi polyhedrons (VPs) <0,0,12,3>, <0,0,12,2>, and Si-centered VPs <0,2,8,2>, <0,2,8,0>. Most initial VPs are distorted at high temperatures due to intense thermal perturbation. A new cluster transformation analysis (CTA) method was proposed to evaluate the stability of ideal VPs against perturbation and predict the possible transformations of the initial VPs in atomic simulations. Most transformations of the initial VPs in βNb5Si3 originate from distortions at the edges of the Nb-centered VPs and the faces/vertices of the Si-centered VPs. The distorted VPs in βNb5Si3 at high temperatures are dominated by <0,1,10,4>, <0,1,10,5>, <0,2,8,1> and <1,2,5,3> VPs, which are predicted as the primary transformations by the CTA.