Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Inhibition of Aspergillus flavus to Reduce Aflatoxin Contamination on Peanuts over Ag-Loaded Titanium Dioxide

Peanuts are susceptible to aflatoxins produced by Aspergillus flavus. Exploring green, efficient, and economical ways to inhibit Aspergillus flavus is conducive to controlling aflatoxin contamination from the source. In this study, Ag-loaded titanium dioxide composites showed more than 90% inhibition rate against Aspergillus flavus under visible light irradiation for 15 min. More importantly, this method could also reduce the contaminated level of Aspergillus flavus to prevent aflatoxins production in peanuts, and the concentrations of aflatoxin B1, B2, and G2 were decreased by 96.02 ± 0.19%, 92.50 ± 0.45%, and 89.81 ± 0.52%, respectively. It was found that there are no obvious effects on peanut quality by evaluating the changes in acid value, peroxide value, and the content of fat, protein, polyphenols, and resveratrol after inhibition treatment. The inhibition mechanism was that these reactive species (•O2−, •OH−, h+, and e−) generated from photoreaction destroyed cell structures, then led to the reduced viability of Aspergillus flavus spores. This study provides useful information for constructing a green and efficient inhibition method for Aspergillus flavus on peanuts to control aflatoxin contamination, which is potentially applied in the field of food and agri-food preservation.
Using Data Augmentation to Improve EEG-Based Prediction of Emergency Braking Intention

Accurate recognition of the driver's emergency braking intention can make the vehicle’ emergency braking system work automatically in advance, thus avoiding some serious traffic accidents. In this paper, we investigated the driver’s emergency braking intention detection based on EEG with data augmentation (DA). We used two electroencephalography (EEG) datasets of the driver’s emergency braking intention detection and performed DA on each of them. Five representative algorithms were used to detect the intention of emergency braking. The results showed that DA significantly improved the prediction accuracy of three convolutional neural networks (ShallowNet, DeepNet and EEGNet) based on deep learning, and slightly improved the prediction performance of the minimum distance to mean based on Riemannian geometry (RMDM) algorithm, but had little impact on the classical common spatial pattern filter combined with linear discriminant analysis (CSP+LDA) algorithm. With DA, EEGNet predicted the impending emergency braking behavior 200ms in advance with an accuracy of over 80% on both datasets. This study provides a framework for detecting drivers’ emergency braking intention based on EEG, which can improve the prediction accuracy by augmenting the EEG data with a small number of samples.
Discovery of a Subparsec Jet 4000 Radii Away from the Central Schwarzschild Engine of NGC 4258

We report the VLBI detection of compact continuum emission associated with the maser disk in NGC 4258. The strongest emission is located about 0.5 mas (0.015 pc) north of the dynamical center of the disk. It has an average flux density of about 3 mJy and varies by ~100% on timescales of weeks. We postulate that we have detected nonthermal synchrotron emission in the base of the northern jet, which is seen on parsec-to-kiloparsec scales in radio to X-ray emission. We also report a detection of emission from the southern jet which may be attenuated by thermal absorption in a layer of ionized gas above the molecular disk. The average flux density of the maser emission in the systemic velocity range is correlated with the flux density of the northern continuum emission. Together with the geometry of the disk, these data suggest that the masers amplify the southern continuum emission and that the southern and northern jet emission are correlated in strength. We discuss the results in the context of jet emission models and properties of other compact radio continuum sources.
A Bayesian phase I/II clinical trial design in the presence of informative dropouts

A phase I/II trial design utilizes both toxicity and efficacy outcomes to make the decision of dose assignment for patients. Because assessing the efficacy endpoint often requires a relatively long follow-up time, phase I/II trials are more susceptible to the missing data problem caused by informative dropouts that are correlated with treatment efficacy and toxicity. In addition, patient outcomes may not be scored quickly enough to apply decision rules that choose treatments or doses for newly accrued patients. To address these issues, we propose a Bayesian phase I/II design that jointly models efficacy, toxicity, and dropout as time-to-event data. Correlations among the three time-to-event outcomes are taken into account by a shared frailty. This joint model strategy accounts for the informative dropouts and has an additional advantage of accommodating a high accrual rate without suspending patient enrollment when toxicity or efficacy outcomes require a long follow-up. Under the Bayesian paradigm, we continuously update the posterior estimate of the model and assign incoming patients to the most desirable dose based on an efficacy-toxicity trade-off utility. Simulation studies show that the proposed design has good operating characteristics with a high probability of selecting the target dose and assigning the most patients to the target dose.
A Piezoelectric Impedance-based Structural Fault Detection by Using Transformer

Electromechanical impedance based (EMI) method is a nondestructive method for detecting structural faults, and the frequency response curve of EMI can effectively show the fault condition of the structure. Transformer neural network has achieved good results in natural language and other fields since it was proposed. In this paper, we simulate different fault states of cantilever beam by changing different bolt loosening and tightening states, and use EMI method to measure the amplitude frequency response curves under different fault conditions, and then use the encoder module of Transformer network and softmax to classify the amplitude data after filtering the response signal. The results show that Transformer network can efficiently classify different fault cases of cantilever beam bolts.
A Two-Step Approach to Overcoming Data Imbalance in the Development of an Electrocardiography Data Quality Assessment Algorithm: A Real-World Data Challenge

Continuously acquired biosignals from patient monitors contain significant amounts of unusable data. During the development of a decision support system based on continuously acquired biosignals, we developed machine and deep learning algorithms to automatically classify the quality of ECG data. A total of 31,127 twenty-s ECG segments of 250 Hz were used as the training/validation dataset. Data quality was categorized into three classes: acceptable, unacceptable, and uncertain. In the training/validation dataset, 29,606 segments (95%) were in the acceptable class. Two one-step, three-class approaches and two two-step binary sequential approaches were developed using random forest (RF) and two-dimensional convolutional neural network (2D CNN) classifiers. Four approaches were tested on 9779 test samples from another hospital. On the test dataset, the two-step 2D CNN approach showed the best overall accuracy (0.85), and the one-step, three-class 2D CNN approach showed the worst overall accuracy (0.54). The most important parameter, precision in the acceptable class, was greater than 0.9 for all approaches, but recall in the acceptable class was better for the two-step approaches: one-step (0.77) vs. two-step RF (0.89) and one-step (0.51) vs. two-step 2D CNN (0.94) (p < 0.001 for both comparisons). For the ECG quality classification, where substantial data imbalance exists, the 2-step approaches showed more robust performance than the one-step approach. This algorithm can be used as a preprocessing step in artificial intelligence research using continuously acquired biosignals.
A coupled method of Laplace transform and Legendre wavelets for nonlinear Klein–Gordon equations

Klein–Gordon equation models many phenomena in both physics and applied mathematics. In this paper, a coupled method of Laplace transform and Legendre wavelets, named (LLWM), is presented for the approximate solutions of nonlinear Klein–Gordon equations. By employing Laplace operator and Legendre wavelets operational matrices, the Klein–Gordon equation is converted into an algebraic system. Hence, the unknown Legendre wavelets coefficients are calculated in the form of series whose components are computed by applying a recursive relation. Block pulse functions are used to calculate the Legendre wavelets coefficient vectors of nonlinear terms. The convergence analysis of the LLWM is discussed. The results show that LLWM is very effective and easy to implement. Copyright © 2013 John Wiley & Sons, Ltd.
Studying word order changes in Latin: some methodological remarks

The main aim of this contribution is to argue that a linear string of Latin words can correspond to more than one syntactic structure, and that this potential for structural ambiguity has important methodological consequences for the synchronic and diachronic study of Latin word order. On the basis of a detailed case study on the much-discussed OV/VO alternation in the history of Latin, it will be shown that whether or not one controls for structural ambiguity is not a theory-internal choice, but that it has major empirical consequences. The conclusion is that quantitative results that emerge from a study that only takes into account syntactically non-ambiguous environments provide a more accurate characterization of the syntactic changes that took place during the evolution from Latin towards the (early) Romance languages.
Three unconventional maxims in the natural selection of cancer cells: Generation of induced tumor-suppressing cells (iTSCs)

Induced tumor-suppressing cells (iTSCs) can be generated from cancer and non-cancer cells. Here, three paradoxical maxims for the action of iTSCs are reviewed: the secretion of tumor-suppressing proteins, their role as a “double-edged” sword, and the elimination of lesser-fit cancer cells. “Super-fit” cancer cells secrete an array of proteins, most of which contribute to enhancing their growth and removing “lesser-fit” cancer cells. These maxims explain the potential dilemma with therapeutic agents since the inhibitory agents tend to promote the synthesis of tumor-promoting proteins. The maxims suggest the possibility of a novel treatment option using cancer-guided evolutionary-fit iTSCs.
Towards Hybrid Electronic-Mechanical Beamforming for IEEE 802.11ad

Electronical beamforming (EBF) is an enabling technology for millimeter-wave (mmWave)-based communication; it is used by standards like IEEE 802.11ad known as WiGig. As WiGig commercial off-the-shelf solutions must be inexpensive, the EBF cannot be freely controlled; instead the beamforming is chosen from a small set of preconfigured configurations, which limits its gain. In this paper, we propose hybrid WiGig (hWiGig), which provides an additional mechanical steering on top of the WiGig’s EBF by enabling physical rotation of the mmWave antenna array. A proof of concept prototype is implemented and used to quantify its gain. Experimental results reveal a large improvement in terms of throughput, especially in NLOS environments with a gain of up to 13.4×. Moreover, random strategies which test only a few physical orientations achieve still high performance gain which underlines hWiGig practical relevance.
COMPTONIZED PHOTON SPECTRA OF SUPERCRITICAL BLACK HOLE ACCRETION FLOWS WITH APPLICATION TO ULTRALUMINOUS X-RAY SOURCES

Radiation spectra of supercritical black hole accretion flows are computed using a Monte Carlo method by post-processing the results of axisymmetric radiation hydrodynamic simulations. We take into account thermal/bulk Comptonization, free–free absorption, and photon trapping. We found that a shock-heated region (∼108 K) appears at the funnel wall near the black hole where the supersonic inflow is reflected by the centrifugal barrier of the potential. Both thermal and bulk Comptonization significantly harden photon spectra although most of the photons upscattered above 40 keV are swallowed by the black hole due to the photon trapping. When the accretion rate onto the black hole is ≈ 200LE/c2, where LE is the Eddington luminosity, the spectrum has a power-law component which extends up to ∼10 keV by upscattering of photons in the shock-heated region. In higher mass accretion rates, the spectra roll over around 5 keV due to downscattering of the photons by cool electrons in the dense outflow surrounding the jet. Our results are consistent with the spectral features of ultraluminous X-ray sources, which typically show either a hard power-law component extending up to 10 keV or a rollover around 5 keV. We found that the spectrum of NGC 1313 X-2 is quite similar to the spectrum numerically obtained for high accretion rate () source observed with low viewing angle (i = 10°–20°). Our numerical results also demonstrate that the face-on luminosity of supercritically accreting stellar mass black holes (10 M☉) can significantly exceed 1040 erg s−1.
A new FPCA-based remaining useful life prediction method for truncated degradation signals

Functional principal component analysis (FPCA) is a commonly used nonparametric degradation modeling technique. A basic requirement of FPCA is that different units must share the same scale in X-axis. A FPCA-based degradation modeling method has been specially designed for truncated degradation signals whose amplitudes are truncated at the failure threshold. This method formulates the degradation time as a function of state level by axis rotation. Since the degradation signals of different units are truncated at the same failure threshold level, the axis rotation strategy ensures the same scale in X-axis. However, in cases of noisy signals, one state level may correspond to multiple time points, which means that the time function depending on state level does not exist. In addition, this method assumes that the noise term as well as the functional principal component (FPC) scores follow the Gaussian distribution, which restricts the flexibility in real practice. Aiming at the above two issues, this paper develops a new degradation modeling method based on the FPCA. The proposed method formulates the degradation model on the definition of first pass time (FPT), which ensures the existence of the time function depending on state level. The noise term and the FPC scores are not restricted to the Gaussian distribution. A particle filtering based updating algorithm is developed to deal with the non-Gaussian state estimation issue of the FPC scores according to the online condition monitoring signals. The effectiveness of the proposed method is verified using an experiment of milling cutter wear.
Inverse association of daily fermented soybean paste (“Jang”) intake with metabolic syndrome risk, especially body fat and hypertension, in men of a large hospital-based cohort

Introduction Jang is a fermented soybean paste containing salt and is traditionally used as a substitute for salt to enhance the flavor of foods in Korea. It has been speculated that regular consumption of Jang may lower the risk of metabolic syndrome (MetS). We hypothesized that Jang intake was associated with the risk of MetS and its components after adjusting for potential confounders, including sodium intake. The hypothesis was investigated according to gender in a large city hospital-based cohort (n = 58,701) in Korea. Methods Jang intake, calculated as the sum of the intakes of Chungkookjang, Doenjang, Doenjang soup, and Ssamjang (a mixture of Doenjang and Kochujang), was included in the semi-quantitative food frequency questionnaire (SQFFQ) administered to the cohort, and the daily Jang intake was estimated. The participants were categorized into low-Jang and high-Jang groups by 1.9 g daily Jang intake. MetS was defined according to 2005 revised United States National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria modified for Asians. Results The participants in the low-Jang and high-Jang groups consumed an average of 0.63 g and 4.63 g Jang daily; their total sodium intake was about 1.91 and 2.58 g/day, respectively. The participants in the high-Jang group had higher energy, fiber, calcium, vitamin C, vitamin D, and potassium intake than those in the low-Jang group. After adjusting for covariates, the participants with the highest sodium intake (≥3.31 g/day) were positively associated with MetS risk in the quintiles of men and women. Among the MetS components, waist circumference, fat mass, and hypo-high-density lipoprotein (HDL)-cholesterolemia were positively associated with sodium intake in all participants and women. Unlike the association seen with sodium intake, Jang intake (≥1.9 g/day) was inversely associated with MetS components, including waist circumference, fat mass, serum glucose concentrations, and hypo-HDL-cholesterolemia in all participants and men, after adjusting for covariates including sodium intake. Discussion Substituting salt for Jang in cooking may be recommended to prevent and alleviate MetS incidence, and its efficacy for MetS risk was better in men than women. The results can be applied to sodium intake in Asian countries where salt is used to promote flavor.
Value Creation for Refugees by Social Partnerships: A Frames Perspective

Refugee crises are one of the grand challenges of the 21st century. Despite the theoretical importance attached to value created for beneficiaries in the partnership literature, research tends to focus on internal processes and value created for partners and partnerships, leading to widespread calls to further specify the value created by partnerships for beneficiaries. Applying an analytical framework from the value creation and social impact literatures, we report on a study of multiple social partnerships of a nongovernmental organization in the refugee issue field. Our results demonstrate that frames of refugees held by partners and in partnerships’ implementation contexts shape the value creation activities undertaken for beneficiaries, and determine whether value is created and what types of value. The dual contribution of this article comprises a rare empirical study of value creation activities for beneficiaries (here, refugees) and theorization of how and when implementation context affects value creation by partnerships.
Combined Description of the Equation of State and Diffusion Coefficient of Liquid Water Using a Two-State Sanchez–Lacombe Approach

Water is one of the most important compounds on Earth, yet its material properties are still poorly understood. Here, we use a recently developed two-state, two-(time)scale (TS2) dynamic mean-field model combined with the two-state Sanchez–Lacombe (SL) thermodynamic theory in order to describe the equation of state (density as a function of temperature and pressure) and diffusivity of liquid water. In particular, it is shown that in a relatively wide temperature and pressure range (160 K < T < 360 K; 0 < P < 100 MPa), density and self-diffusion obey a special type of dynamic scaling, similar to the “τTV” scaling of Casalini and Roland, but with the negative exponent γ. The model predictions are consistent with experimental data. The new equation of state can be used for various process models and generalized to include multicomponent mixtures.
An LSTM and Encoder-Decoder-Based Approach for Runoff Prediction

Runoff time series are characterized by nonlinearity, complexity, and variability. Effective runoff prediction can improve the information and intelligence of local water resources management. To improve the accuracy of daily runoff prediction, this paper addresses the multidimensional data of hydrological data and multidimensional correlation meteorological factors to make multi-step prediction, and this paper proposes a sequence-to-series daily runoff prediction model based on encoder-decoder of long and short term memory network. To verify the validity of the model, six years of daily runoff data (2012-2017) and related meteorological factor data and three evaluation indexes (root mean square error, mean absolute error, and coefficient of determination) from the Punan hydrological station site in the Jiulong River basin of Fujian Province are selected for testing. The prediction results show that the proposed model has higher accuracy than the existing runoff prediction models such as gradient boosting (XGBoost), decision tree (DTA), support vector machine (SVR), convolutional neural network (CNN), recurrent neural network (RNN), and long-short time neural network (LSTM).
Transcriptomic Establishment of Pig Macrophage Polarization Signatures

Macrophages are the foremost controllers of innate and acquired immunity, playing important roles in tissue homeostasis, vasculogenesis, and congenital metabolism. In vitro macrophages are crucial models for understanding the regulatory mechanism of immune responses and the diagnosis or treatment of a variety of diseases. Pigs are the most important agricultural animals and valuable animal models for preclinical studies, but there is no unified method for porcine macrophage isolation and differentiation at present; no systematic study has compared porcine macrophages obtained by different methods. In the current study, we obtained two M1 macrophages (M1_IFNγ + LPS, and M1_GM-CSF) and two M2 macrophages (M2_IL4 + IL10, and M2_M-CSF), and compared the transcriptomic profiles between and within macrophage phenotypes. We observed the transcriptional differences either between or within phenotypes. Porcine M1 and M2 macrophages have consistent gene signatures with human and mouse macrophage phenotypes, respectively. Moreover, we performed GSEA analysis to attribute the prognostic value of our macrophage signatures in discriminating various pathogen infections. Our study provided a framework to guide the interrogation of macrophage phenotypes in the context of health and disease. The approach described here could be used to propose new biomarkers for diagnosis in diverse clinical settings including porcine reproductive and respiratory syndrome virus (PRRSV), African swine fever virus (ASFV), Toxoplasma gondii (T. gondii), porcine circovirus type 2 (PCV2), Haemophilus parasuis serovar 4 (HPS4), Mycoplasma hyopneumoniae (Mhp), Streptococcus suis serotype 2 (SS2), and LPS from Salmonella enterica serotype minnesota Re 595.
ENHANCEMENT OF TURBULENCE IN TOKAMAKS BY MAGNETIC FLUCTUATIONS

Three-dimensional nonlinear simulations of drift-ballooning modes in a torus including electromagnetic effects are described. When diamagnetic effects are weak, the self-consistent magnetic fluctuations in the model lead to a drastic enhancement of the transport well below the ideal ballooning instability limit. {copyright} {ital 1997} {ital The American Physical Society}
An Improvement of DV-Hop Localization Algorithm Based on Cyclotomic Method in Wireless Sensor Networks

Location information is one of the crucial and essential elements for monitoring data in wireless sensor networks. The distance vector-hop (DV-Hop) localization algorithm is of practical importance in improving its localization performance. To achieve global optimization, a DV-Hop algorithm based on the cyclotomic method and weighted normalization, also known as CMWN-DV-Hop, is nominated in this paper. Therefore, the segmentation and weighting factors are introduced and normalized. The weighted recursive least-squares (WRLS) algorithm is chosen to compute the coordinates of the unknown nodes. The effects of various factors on this algorithm are tested, including the number of nodes, the anchor node ratio, and the communication radius. The simulation results show that the proposed algorithm has a super performance in reducing the localization error.