Miracle-dz/UniFAR-FTUs huggingface dataset:
Porcine Respirovirus 1 Suppresses Host Type I Interferon Production and the JAK-STAT Signaling Pathway

Porcine respirovirus 1 (PRV1), first reported in Hong Kong, is currently widely spread in several countries. Our knowledge of the clinical significance and the pathogenicity of this virus is still limited. In this study, we studied the interactions between PRV1 and host innate immune responses. PRV1 exhibited strong inhibitory effects on the production of interferon (IFN), ISG15, and RIG-I induced by SeV infection. Our data generated in vitro suggest that multiple viral proteins can suppress host type I interferon production and signaling, including N, M, and P/C/V/W. The P gene products disrupt both IRF3 and NF-κB dependent type I IFN production and block type I IFN signaling pathway by sequestering STAT1 in the cytoplasm. The V protein disrupts both MDA5 signaling and RIG-I signaling through interaction with TRIM25 and RIG-I, V protein blocks RIG-I polyubiquitination, which is required for RIG-I activation. V protein also binds to MDA5, which may contribute to its inhibitory effect on MDA5 signaling. These findings indicate that PRV1 antagonizes host innate immune responses using various mechanisms, which provides important insights into the pathogenicity of PRV1.
Causality Analysis with Information Geometry: A Comparison

The quantification of causality is vital for understanding various important phenomena in nature and laboratories, such as brain networks, environmental dynamics, and pathologies. The two most widely used methods for measuring causality are Granger Causality (GC) and Transfer Entropy (TE), which rely on measuring the improvement in the prediction of one process based on the knowledge of another process at an earlier time. However, they have their own limitations, e.g., in applications to nonlinear, non-stationary data, or non-parametric models. In this study, we propose an alternative approach to quantify causality through information geometry that overcomes such limitations. Specifically, based on the information rate that measures the rate of change of the time-dependent distribution, we develop a model-free approach called information rate causality that captures the occurrence of the causality based on the change in the distribution of one process caused by another. This measurement is suitable for analyzing numerically generated non-stationary, nonlinear data. The latter are generated by simulating different types of discrete autoregressive models which contain linear and nonlinear interactions in unidirectional and bidirectional time-series signals. Our results show that information rate causalitycan capture the coupling of both linear and nonlinear data better than GC and TE in the several examples explored in the paper.
The Enhancement of CO Oxidation Performance and Stability in SO2 and H2S Environment on Pd-Au/FeOX/Al2O3 Catalysts

Carbon monoxide (CO) is a colourless, odourless, and toxic gas. Long-term exposure to high concentrations of CO causes poisoning and even death; therefore, CO removal is particularly important. Current research has focused on the efficient and rapid removal of CO via low-temperature (ambient) catalytic oxidation. Gold nanoparticles are widely used catalysts for the high-efficiency removal of high concentrations of CO at ambient temperature. However, easy poisoning and inactivation due to the presence of SO2 and H2S affect its activity and practical application. In this study, a bimetallic catalyst, Pd-Au/FeOx/Al2O3, with a Au:Pd ratio of 2:1 (wt%) was formed by adding Pd nanoparticles to a highly active Au/FeOx/Al2O3 catalyst. Its analysis and characterisation proved that it has improved catalytic activity for CO oxidation and excellent stability. A total conversion of 2500 ppm of CO at −30 °C was achieved. Furthermore, at ambient temperature and a volume space velocity of 13,000 h−1, 20,000 ppm CO was fully converted and maintained for 132 min. Density functional theory (DFT) calculations and in situ FTIR analysis revealed that Pd-Au/FeOx/Al2O3 exhibited stronger resistance to SO2 and H2S adsorption than the Au/FeOx/Al2O3 catalyst. This study provides a reference for the practical application of a CO catalyst with high performance and high environmental stability.
A prognostic turbulence scheme for the nonhydrostatic plume model ATHAM

The presented turbulence scheme was developed for the Active Tracer High-Resolution Atmospheric Model (ATHAM) to parameterize the effect of subgrid-scale turbulence. In contrast to the commonly used assumption of local isotropy in high-resolution atmospheric modeling, this scheme differentiates between horizontal and vertical turbulent exchange to represent the strong influence of buoyancy forces and vertical transports. Its computational efficiency is similar to classical turbulent kinetic energy approaches while preserving one of the main feature of higher-order schemes. The present extensions to include anisotropic effects in a turbulent kinetic energy approach do not need any ad hoc assumptions and are equivalent to the classical formulation in the isotropic limit. The presence of high tracer concentrations in a particle-laden plume is taken into account, as well as supersonic effects at low Mach numbers. The turbulent exchange coefficients used in the equations of motion are derived from a set of three coupled differential equations for the horizontal and vertical turbulent energy and the turbulent length scale. No turbulent equilibrium is assumed. All turbulent quantities are treated prognostically. Numerical simulations of convective plumes of a typical Plinian volcanic eruption with the nonhydrostatic plume model ATHAM reveal that a complex treatment of turbulent quantities is necessary in order to capture the bulk characteristics of the plume, such as the plume height, the horizontal extent, and plume development in time. Anisotropic effects of turbulence have a significant impact on the stability and internal structure of the plume. For the first time, results from a fully three-dimensional simulation of a volcanic plume are presented. Because of its general formulation the presented turbulence scheme is suitable for a wide range of atmospheric applications.
Continuous Inertial Focusing and Separation of Particles by Shape

Mahdokht Masaeli,* Elodie Sollier, Hamed Amini, Wenbin Mao, Kathryn Camacho, Nishit Doshi, Samir Mitragotri, Alexander Alexeev, and Dino Di Carlo Department of Bioengineering, University of California, Los Angeles, 410 Westwood Plaza, Los Angeles 90095, California, USA California NanoSystems Institute, University of California, Los Angeles 90095, California, USA Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 771 Ferst Drive Northwest, Atlanta 30332, Georgia, USA Department of Chemical Engineering, University of California, Santa Barbara,, Mail Code 5080, Santa Barbara 93106, California, USA (Received 20 March 2012; revised manuscript received 15 May 2012; published 12 September 2012)
On Doubly-filled CP and DP Constructions in the History of English

This study pertains to a parallelism between the sentence (CP) and the noun phrase (DP) observed in the history of English. We begin by reviewing that doubly-filled COMP constructions were legitimate in Middle English (ME) and Early Modern English (EModE), though they are excluded by the familiar doubly-filled COMP filter in the Present-day English (PDE). Actually, it will be demonstrated, a historical change comparable to that is attested in the noun phrase as well. We propose that this similarity can be captured by postulating a doubly-filled DP filter, a DP counterpart of doubly-filled COMP filter. A theoretical basis for this pair of filters is established by assuming that AGR was present in phonetically overt C and D at the early stages of English, whereas it is absent in both of their PDE counterparts; if Spec-head agreement is triggered by AGR, it fails in the latter case, which amounts to what is banned by the proposed filters.
Manipulação do Disclosure para Reparação da Imagem Corporativa Após um Desastre Ambiental: Um Estudo do Impacto do Rompimento da Barragem nos Relatórios de Sustentabilidade da Samarco

RESUMO O objetivo desta pesquisa foi analisar de que forma os Relatórios de Sustentabilidade (RS) são manipulados estrategicamente após a ocorrência de um acidente ambiental, visando à neutralização de danos e reparação de imagem da empresa. É uma pesquisa de natureza qualitativa, tendo sido realizada coleta de dados por meio de técnica de pesquisa documental, a partir de consultas aos laudos do IBAMA, notícias veiculadas na imprensa, e RS publicados pela empresa Samarco S. A. , envolvida em um grande desastre ambiental ocorrido em novembro de 2015. A análise do disclosure ambiental de textos e imagens expôs as contradições e a inconsistência dos relatos dos RS com relação a laudos de órgãos oficiais e às notícias divulgadas na mídia. Verificou-se que o acidente ambiental impactou severamente o disclosure ambiental da empresa, modificando padrões de relato a partir de estratégias de dissuasão com vistas a omitir, e ao mesmo tempo reparar a realidade objetiva. Assim, a empresa recorreu à argumentação defensiva por meio da qual procurou eximir-se de responsabilidade, com a manipulação do disclosure ambiental negativo, a fim de desviar a atenção do público externo de informações adversas.
Density Functional Approach for Fermion-Boson-Mixtures in Thomas-Fermi Approximation

Density functional theory has been used extensively for the description of boson and fermion gases, but only few studies are available for mixtures of bosons and fermions where two fermions can form bosons as a diatomic molecule. In this thesis, we propose an explicit form of the energy functional underlying the conversion process and explore the particle densities in various parameter regimes.
Probabilistic Forecasts of Precipitation in Terms of Quantiles Using NWP Model Output

Abstract At sites with observations it is often possible to improve or enrich NWP model forecasts by means of statistical methods. Such forecasts are almost exclusively deterministic or probabilities of discrete events. In this paper a flexible approach for making reliable precipitation forecasts in terms of quantiles is described. The approach is essentially in two steps: (i) estimation of probability of precipitation and (ii) estimation of selected quantiles in the distribution of precipitation amounts given occurrence of precipitation. Estimates are obtained by means of probit regression and local quantile regression, respectively. By applying the laws of probability, the steps are combined to make unconditional quantile forecasts. Examples of daily precipitation forecasting using single deterministic forecasts and ensemble forecasts as input are given.
Cascaded Convolution-Based Transformer With Densely Connected Mechanism for Spectral–Spatial Hyperspectral Image Classification

Hyperspectral image (HSI) classification attempts to classify each pixel, which is an important means of obtaining land-cover knowledge. HSIs are cubic data with spectral–spatial knowledge and can generally be considered as sequential data alongside spectral dimension. Unlike convolutional neural networks (CNNs), which mainly focus on local relationship models in images, transformers have been shown to be a powerful structure for qualifying sequence data. However, it lacks the excellent ability of CNNs in establishing local relationships in images and cannot perform good generalization in the case of insufficient data. In addition, the gradient disappearance problem hinders the convergence stability of deep learning networks as the layers get deeper. To address these problems, we propose a Cascaded Convolution-based Transformer with Densely Connected Mechanism (CDCformer) for HSI classification. First, we propose a cascaded convolution feature tokenization to extract spectral–spatial information, which will introduce some inductive bias properties of CNN into the transformer. In addition, we design a simple and effective densely connected transformer to enhance feature propagation and transfer memorable information from shallow to deep layers. It efficiently improves the performance of the transformer and extracts more discriminative spectral–spatial features from the HSI. Extensive experimental evaluation of three public hyperspectral datasets shows that CDCformer achieves competitive classification results.
Electricity Consumption and Population Growth in South Africa: A Panel Approach

This study investigates the relationship between population growth and electricity consumption in South Africa for the period from 2002 to 2021 collected from StatsSA. The study utilises Seemingly Unrelated Regression model and Dumitrescu and Hurlin (2012) causality tests to analyse the relationship between the variables. Empirical results revealed that there is a negative statistically significant relationship between population growth and electricity consumption in South Africa. The results further reveal one-way causality running from population growth to electricity consumption. The study recommends that the government and policy makers must implement policies aimed at increasing renewable electricity generation to match the gap between electricity demand and growing population thereby reducing constant loadshedding in South Africa.
Striation-Based Broadband Source Localization Using a Particle Velocity Sensor in Deep Water

Depth-related striations in the acoustic intensities due to interference between the direct and sea surface reflected arrivals have been exploited to estimate the source depth by vertical arrays deployed near the bottom of the deep sea. This article presents an efficient technique to predict the constructive and destructive interference striation patterns observed in the broadband acoustic intensities for both the pressure and particle velocity (PV) fields. The consistency of striation patterns for the pressure and PV fields provides a basis for striation matching methods extended from the pressure array to a PV sensor array or an acoustic vector sensor array. To estimate the source depth, 1- and 2-D striation pattern matching methods have been proposed, which match the measured striations with the replica over either the frequency or the source range for each hypothesized source depth. Their effectiveness and performance have been evaluated through simulations and experimental data of a PV sensor in deep water. The results demonstrate that they have a narrow main lobe width and a good performance under a low signal-to-noise ratio and are applicable for shallow and distant sources when the number of interference striations is few.
CNN–LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy

Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN–LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN–LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN–LSTM neural network combined with IR spectroscopy helps to identify different pasta products.
A Wearable mm-Wave Radar Platform for Cardiorespiratory Monitoring

A chest-wearable 60--GHz radar system is presented for continuous extraction of cardiorespiratory displacement waveforms as well as the respiratory rate (RR), heart rate (HR), and HR variability (HRV). The radar antenna's performance was investigated while in close proximity to the skin using full-wave electromagnetic simulations. Investigations using the adhesive chest-mounted prototype extract detailed cardiorespiratory displacement waveforms. An accuracy of over 96% is achieved for RR, while HR and HRV were extracted with accuracies of over 96% and 95%, respectively.
Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment

A Cyber-Physical System (CPS) is a network of cyber and physical elements that interact with each other. In recent years, there has been a drastic increase in the utilization of CPSs, which makes their security a challenging problem to address. Intrusion Detection Systems (IDSs) have been used for the detection of intrusions in networks. Recent advancements in the fields of Deep Learning (DL) and Artificial Intelligence (AI) have allowed the development of robust IDS models for the CPS environment. On the other hand, metaheuristic algorithms are used as feature selection models to mitigate the curse of dimensionality. In this background, the current study presents a Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-based Intrusion Detection (SCAVO-EAEID) technique to provide cybersecurity in CPS environments. The proposed SCAVO-EAEID algorithm focuses mainly on the identification of intrusions in the CPS platform via Feature Selection (FS) and DL modeling. At the primary level, the SCAVO-EAEID technique employs Z-score normalization as a preprocessing step. In addition, the SCAVO-based Feature Selection (SCAVO-FS) method is derived to elect the optimal feature subsets. An ensemble Deep-Learning-based Long Short-Term Memory–Auto Encoder (LSTM-AE) model is employed for the IDS. Finally, the Root Means Square Propagation (RMSProp) optimizer is used for hyperparameter tuning of the LSTM-AE technique. To demonstrate the remarkable performance of the proposed SCAVO-EAEID technique, the authors used benchmark datasets. The experimental outcomes confirmed the significant performance of the proposed SCAVO-EAEID technique over other approaches with a maximum accuracy of 99.20%.
Security Enhancement System Based on the Integration of Cryptography and Steganography

Nowadays, due to the widespread use of data exchange in electronic way, the information security has become important in data storage and transmission. Lack of security makes many problems in security awareness applications. The two kinds of information that are widely used in daily communication are image and text. Steganography and cryptography are both ways to protect the data security against various attacks. Thus, this work focuses on the enhancement of not only image but also information security based on the combination of cryptography and steganography methods. From the cryptography point of view, image security is enhanced based on the combination of proposed block-based transformation and encryption technique. Firstly, the original image is transformed with proposed block-based transformation algorithm to obtain better robustness of image encryption. And then, the generated transformed image is encrypted by using Blowfish encryption algorithm. After getting the encrypted image, a steganography approach provides the data hiding system by using this encrypted image as a cover for information security. Moreover, to prove the advantages of using combination process (proposed transformation and Blowfish encryption) rather than single encryption, performance comparison is made by calculating the correlation and entropy of encrypted images generated by combination process and Blowfish encryption algorithm.
EIGENVALUE LOCATIONS OF GENERALIZED COMPANION

Generalized predictor companion matrices arise in the linear prediction approach for the t of a weighted sum of n exponentials to a given set of data points. They are special solutions of matrix equations of the type H(l + p)S = H(l), where for each l 0 H(l )i s anM N Hankel matrix obtained from this data (M N> n). We discuss in this paper results about the eigenvalue locations of this class of solutions by means of linear algebra techniques. An application of these results in the case that all the exponents have either negative or positive real parts is that the n exponentials can correspond to eigenvalues which are outside the unit circle depending on the choice of generalized predictor companion matrices. The other (N in) eigenvalues of these matrices always lie inside the unit circle and approach zero when p increases. This separation can facilitate their numerical calculation.
Two dimensional supersonic solutions for the steady Euler-Poisson system in a bounded domain

We establish the unique existence and stability of supersonic flow for steady Euler-Poisson system in a two dimensional rectangular domain when prescribing pressure, incoming flow angles, and normal electric field at the entrance. With the aid of Helmholtz decomposition for the velocity field, the supersonic flows of the Euler-Poisson system are obtained as solutions to a nonlinear system consisting of a second order hyperbolic-elliptic coupled system and several transport equations. The new feature of this work is that we achieve the well-posedness of a boundary value problem associated with a coupled system consisting of two second order quasilinear equations, where one equation is hyperbolic and the other is elliptic, via a deliberate choice of multiplier to gain energy estimate.
Research on the Spring Creep Based on the Load Simulator of the Double Torsion Spring Steering Gear

In this paper, creep at room temperature is studied using a mechanical double−spring steering−gear load table, and the results are used to determine the accuracy of theoretical and simulated data. A creep equation at room temperature, based on the parameters obtained by a new macroscopic tensile experiment method, is used to analyze the creep strain and creep angle of a spring under force. The correctness of the theoretical analysis is verified by a finite−element method. Finally, a creep strain experiment of a torsion spring is carried out. The experimental results are 4.3% lower than the theoretical calculation results, which demonstrates the accuracy of the measurement, with an error of <5% achieved. The results shows that the equation used for the theoretical calculation is highly accurate and can meet the requirements of engineering measurement.
An Automated Deep Learning-Based Software for Total Lung Volume Calculation and Lung 3D Model Reconstruction

In this paper, we propose an automated deep learning-based software, especially for reconstructing 3D lung images and estimating Total Lung Volume (TLV). The system is mainly designed for Vietnamese users with Vietnamese as the default language. The purpose of our study is to build an automated system based on deep learning and machine-learning models to measure TLV and construct a 3D prototype of the lung. The training data is collected from The Cancer Imaging Archive (TCIA) dataset, provided by the 2017 Lung CT Segmentation Challenge for training and testing the proposed model. Our proposed system utilizes a modified Bi-directional Convolutional (ConvLSTM) U-Net (BCDU-Net) neural network. We use the lung segmentation results as the data to build 3D lung image and calculate the lung volume. We test the reliability of the software with real dataset collected from Bach Mai hospital. The overall results have shown that Accuracy against actual data from commercial products is approximately 99% when using our model to calculate the TLV.