Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytochemical Profiling, Antioxidant Activity, and Protective Effect against H2O2-Induced Oxidative Stress of Carlina vulgaris Extract

Carlina vulgaris is a little-understood plant with unexplored biological potential, and the papers regarding its chemical composition are scarce. In our study, for the first time, the phytochemical profile of the plant, focusing on polar metabolites, was established using modern chromatographic techniques including LC-HRMS-QTOF-CAD, UHPLC-PDA-MS. Phytochemical analysis revealed that the species is a rich source of polyphenolic components, with the most abundant being chlorogenic acid and C-glycosides of luteolin, including carlinoside, orientin, isoorientin, and C-glycosides of apigenin, schaftoside, isoschaftoside, and vitexin. Furthermore, we assessed the impact of the polyphenolic-rich fraction of C. vulgaris extracts on human skin fibroblasts using the MTT and NR assays. It was found that the extract was non-toxic and exhibited potent antioxidant activity in the cells subjected to induced oxidative stress. Additionally, it effectively protected the cells against H2O2-induced cytotoxicity. Our study contributes to the general trend of searching for new phytotherapeutics with potential applications in pharmacy and medicine. The results indicate that further exploration of C. vulgaris species is worthwhile, as they can serve as valuable plant material for cosmetic use.
Microbial community and network differently reshaped by crushed straw or biochar incorporation and associated with nitrogen fertilizer level

Straw returning has been demonstrated as a beneficial approach for the utilization of renewable biomass source, which contributes to reducing environmental pollution and strengthening the sustainability of agriculture. However, information on how microorganisms respond to different straw return modes (SRMs) at varying nitrogen fertilizer levels (NFLs) in the black soil is still limited. The community composition, network pattern, and modular function of bacteria and fungi are investigated under three SRMs, including straw removal (CK), crushed straw incorporation (SD), and biochar incorporation (BC) at three NFLs (0, 144, and 240 kg N ha−1, respectively) mainly using Illumina MiSeq technique based on a long‐term maize field experiment. Results showed that bacterial richness, diversity, and fungal richness decreased with NFL reduction. However, these decreases can be compensated by SD and BC, demonstrating superiority for BC at reduced NFLs. SD and BC differed in their effects on the bacterial and fungal abundances (showing increments only in SD) and fungal Shannon diversity (remaining stable only in BC irrespective of NFLs). Microbial communities were substantially affected by SRMs and interacted with NFLs, which were driven by soil NH4+‐N, available potassium, total nitrogen, and pH. In addition, SD induced a network characterized by its highly complex (average degree 10.259 vs. 3.364) and stable structure (average clustering coefficient 0.503 vs. 0.239), Ascomycota as predominating keystone taxa, and abundant N‐cycling related bacteria, while BC formed a network comprising a superior modular structure (modularity 2.599 vs. 0.912), dominant symbiotic fungi, and soil bulk density as specific shaping factor, indicating that network pattern, keystone taxa, modular function, and determining factors shifted between SD and BC co‐occurrence networks. These results deepen insights into the response divergence of bacteria and fungi to SRMs and NFLs, providing a scientific basis for selecting the suitable strategy for sustainable straw utilization in the black soil area.
Constant-Parameter Voltage-Behind-Reactance Synchronous Machine Models Considering Main Flux Saturation for EMTP-Type Programs

The state-of-the-art saturable phase-domain (PD) and voltage-behind-reactance (VBR) synchronous machine models have been recently considered in the literature as reliable and accurate alternatives to the traditional qd0 models. However, due to the rotor-position- and saturation-dependent interfacing circuit, implementing these models in electromagnetic transient (EMT) simulators has been challenging due to the need to refactorize the machine-network conductance matrix at each time step during simulation. This article presents two new VBR-based synchronous machine models with main flux saturation that yield constant interfacing conductance matrix in EMTP-type solution. Case studies in PSCAD/EMTDC verify that the proposed models demonstrate numerical accuracy similar to the state-of-the-art models, while achieving significant computational gains in multimachine systems.
A Physically-Based Spatio-Temporal Sky Model

In this work we present a physically-based optical model of the atmosphere, that takes into account the seasonal and geographic variation of its composition. Based on data from the atmospheric science literature, we build a highly detailed the composition of the atmosphere, and how it varies depending on the position of the observer, or the time of the year. Then, based on precise measurements of the optical properties of the components of the atmosphere, we map our model into a radiative model, which can be rendered in any existing volumetric renderer. We demonstrate our model in multispectral renders of daylight sky-domes, showing the changes in the appearance occurring when varying the season or location of the observer.
Intermittency in delay-coupled FitzHugh–Nagumo oscillators and loss of phase synchrony as its precursor

We study the dynamical properties of in-out intermittency in a system of two identical FitzHugh– Nagumo oscillators coupled by multiple time delays. In this system, the intermittency is manifested as irregular switching between a nearly synchronous state with small and large amplitude chaotic oscillations and a highly asynchronous state with a single large amplitude oscillation. We show that loss of phase synchrony significantly prior to the occurrence of the asynchronous large amplitude oscillation can be used as a precursor to the switching of states in such systems.
Robust M estimation of parameters in a linear system

Abstract In this paper, robust M-estimation of parameters in a linear system is studied. Some strict theoretical analysis and algorithm analysis about the linear system are given. The results show that the algorithm is simple, and the amount of computation is affected by the number of data is not very large. The results are much better than the least square estimation.
Study on Purification Efficiency of Novel Aquatic Plant Combinations and Characteristics of Microbial Community Disturbance in Eutrophic Water Bodies

Aquatic plant restoration is an important technique for the treatment of eutrophic water bodies. There are significant differences in pollutant removal efficiency among different combinations of aquatic plant species in eutrophic water bodies. Therefore, further research on the selection of suitable combinations of aquatic plant species is of great significance for the restoration of eutrophic water bodies. This study investigated the pollutant removal efficiency and bacterial community structure of three novel combinations of aquatic plants, including Lythraceae, Nymphaea, and Myriophyllum (LNM group), Lythraceae, Nymphaea, and Hydrilla verticillata (LNH group), and Lythraceae, Nymphaea, and Vallisneria (LNV group), as well as a control group (CK group). The components of the CK group were only sediment and culture water without any plants. The results show that on one hand, the LNH group had the highest removal rate of COD (90.29%); the LNV group exhibited the highest removal rates for NH4+-N and TN, with removal rates of 61.20% and 82.94%, respectively; and there was no significant difference in the removal rate of TP among the experimental groups, except for the LNH group, which showed higher initial removal efficiency for TP. On the other hand, plant combinations had different impacts on the top 13 dominant microflora at the phylum level. Proteobacteria and Actinobacteria showed the highest removal efficiency for COD in the LNH group, while Verrucomicrobi, Chloroflex, and Acidobacteria showed higher removal efficiency for NH4+-N and TN in the LNV and LNH groups. In summary, the three different combinations of aquatic plants exhibited distinct pollutant removal characteristics, significantly altered the structure of the microbial community, and provided a theoretical basis for their practical application in the restoration of eutrophic water bodies.
Joint User Scheduling, Base Station Clustering, and Beamforming Design Based on Deep Unfolding Technique

In this paper, we investigate joint user (UE) scheduling, base station (BS) clustering and beamforming design in a dense network. To reduce the computation burden, we propose a deep unfolding UE scheduling, BS clustering and beamforming (DU-USBCB) method based on the iterative weighted minimum mean square error (WMMSE) algorithm, where the UE scheduling and BS clustering are represented by the group sparsity of the transmit beamforming vectors. The proposed DU-USBCB neural network layer comprises receive coefficient, weight and transmit beamforming vector modules, the former two of which have closed-form expressions. For the third module, the group sparse transmit beamforming vectors are obtained by multiple steps of projected gradient descent and nonlinear sparsification, where the step-sizes are learned through unsupervised learning. We also propose a distributed UE selection (DUS) algorithm, which helps reduce the computation workload. Simulation results verify the effectiveness of the proposed DU-USBCB and DUS methods. The learned step-sizes are directly applied in the testing scenarios with different numbers of UEs, BSs and antennas as well as incomplete channel state information. Besides, our proposed methods can achieve comparable performance but with about 53% and 89% computation reduction compared to the RSRP-WMMSE and SWMMSE methods respectively.
Current Status of Jet Engines and Their Future on Fuel Efficiency

Recently, regulations on emissions for international aviation has become increasingly strict. In addition, cost of fossil fuel has increased owing to limited non-renewable resource. Therefore, it is important to understand fuel efficiency because this is a practical method to reduce the cost and emissions based on recent technology. In this article, theoretical background of jet engines will be demonstrated, which include structure of jet engines and cycle efficiency. Followed by factors that affect the efficiency of jet engines. Finally, applications of jet engines based on efficiency and methods that improve efficiency are demonstrated, which includes recent technologies and possible future directions.
Heterogeneous Precipitation of the α-Phase in Ti15Mo Alloy Subjected to High Pressure Torsion

Ti15Mo alloy is a binary metastable beta titanium alloy with excellent mechanical properties and biocompatibility. Therefore, it can potentially replace the most commonly used Ti6Al4V alloy in biomedical applications. Further processing of the alloy by the methods of severe plastic deformation provides an opportunity to achieve exceptional grain refinement and to enhance mechanical properties. Another way how to increase the strength of the alloy is thermomechanical processing which leads to the formation of the α-phase. α-phase is known to precipitate heterogeneously, in particular at grain boundaries. Refinement of parent β matrix therefore affects the size and the morphology of α precipitates. Ultrafine grained Ti15Mo alloy was processed by high pressure torsion. The precipitation behavior of ultrafine grained and benchmark coarse-grained material during isothermal ageing was investigated by ex situ scanning electron microscopy. In the coarse-grained material the α-phase precipitates both as grain boundary α-phase and as acicular α-phase inside the β-grains. On the other hand, in ultrafine grained alloy, only equiaxed sub-micrometer α particles precipitated at dense network of grain boundaries.
Effect of Pre and Postoperative Nursing Intervention on Activities of Daily Living and Quality of life among Patients undergoing Hip Replacement

: The patient with hip replacement becomes a chronic patient who require a successful recovery post surgery. Successful recovery requires not only proper surgical technique but also effective pre and postoperative nursing care to improve their activity of daily livings and enhance their quality of life. Purpose: To determine the effect of pre and postoperative nursing intervention on activities of daily livings and quality of life among patients undergoing hip replacement. Setting: The study was conducted at orthopedic departments at Menoufia University and Shebin El-Kom teaching hospitals. Sampling: A consecutive sample of 80 adult patients who were planned for hip replacement surgery was assigned randomly and alternatively into two equal groups, 40 patients for each group. Instruments: Two instruments were used for data collection: Structured interview questionnaire and hip disability and osteoarthritis outcome score. Results: Pre intervention about half of both study and control groups had sever hip disability related function and activity of daily living scores (55.0 % and 57.5% % respectively) that were highly significantly improved among study group to none complains at follow up period (60.0% of study group compared to 5.0% of control group for function and activity of daily living score respectively). Also pre intervention about half of both study and control groups had sever hip disability related quality of life score (47.5 % and 52.5% respectively) that were highly significantly improved among study group to none complains at follow up period (72.5% compared to 7.5% for quality of life score respectively) . Conclusions: Pre and postoperative nursing intervention has a positive impact on improving activities of daily living and quality of life among patients undergoing hip replacement. Recommendations: Supervised health teaching regarding pre and postoperative care should be given for hip replacement patients to improve their activities of daily livings and quality of life.
Cellular Nonlinear Network Circuit Model with Application to Seizures Prediction

FitzHugh Nagumo circuit models activation and deactivation dynamics of a spiking neuron. The proposed model in this paper is presented by a reaction-diffusion autonomous cellular nonlinear network. The dynamics of the obtained network is studied and bifurcation analysis is provided. Based on the obtained theoretical results we apply FitzHugh Nagumo circuit model to seizure prediction. The spatio-temporal EEG analysis is provided.
State estimation of the dynamic behavior of plasma properties in a Hall effect thruster discharge

The dynamic behaviors of the plasma properties in a Hall effect thruster discharge channel are estimated using a physics-constrained extended Kalman filter (PC-EKF) with a zero-dimensional global plasma model. The state estimates obtained using the global plasma EKF model are augmented by experimental data, in this case a discharge current signal. The use of state estimation allows for the determination of time-resolved plasma behaviors that may be difficult to measure experimentally, such as electron temperature and bulk velocity. The model is applied to investigate the plasma behavior at various thruster operating conditions, i.e. at varying discharge voltages and discharge currents. The estimated plasma properties are used to determine the classical electron collision frequency and the anomalous electron scattering frequency, which are shown to vary in time.
Boosting for statistical modelling-A non-technical introduction

Boosting algorithms were originally developed for machine learning but were later adapted to estimate statistical models—offering various practical advantages such as automated variable selection and implicit regularization of effect estimates. The interpretation of the resulting models, however, remains the same as if they had been fitted by classical methods. Boosting, hence, allows to use an advanced machine learning scheme to estimate various types of statistical models. This tutorial aims to highlight how boosting can be used for semi-parametric modelling, what practical implications follow from the design of the algorithm and what kind of drawbacks data analysts have to expect. We illustrate the application of boosting in the analysis of a stunting score from children in India and a high-dimensional dataset of tumour DNA to develop a biomarker for the occurrence of metastases in breast cancer patients.
Application of K-Means Algorithm on Normalized and Standardized Data for Type 2 Diabetes Subclusters

Current discovery indicates that Type 2 Diabetes (T2D) could be categorized into many sub-clusters, which is a step towards precision medicine. Implementation of feature scaling to cluster T2D into subgroups is crucial, aiming to transform and fit the data within a specific scale. This paper aims to compare the differences between cleaned, normalized, and standardized data in k-means clustering using T2D patient data. Two data transformation approaches were applied on the clustering algorithm, namely data normalization and data standardization. By comparing the clustering analysis results, normalized data (Elbow method (EM) =435.63) illustrates the best data point distribution to form clusters and good internal clustering validation scores in comparison to cleaned data (EM =502,254.97) and standardized data (EM = 23,518.82). We concluded that data normalization in the k-means clustering algorithm is the best method for data transformation for T2D sub-clustering compared to cleaned data and standardized data.
SOFC and MCFC: New Energy Technologies of The Future

The new energy technology is the hot spot and key point to solving the current environmental problems. In this paper, two types of fuel cells, SOFC and MCFC, were introduced in order to show their contributions to the energy transition. The two kinds of fuel cells were described in detail from the aspects of working principle, power generation mechanism, engineering application, current research and future prospect. Finally, the two fuel cells were compared in terms of electrochemical reaction and outstanding advantages.
Efficient Region Proposal Extraction of Small Lung Nodules Using Enhanced VGG16 Network Model

The efficiency of state-of-the-art convolutional networks trained to detect lung cancer nodules depends on their feature extraction model. Various feature extraction models have been proposed based on convolutional networks, such as VGG-Net, or ResNet. It has been demonstrated that such models effectively extract features from objects in an image. However, their efficacy is limited when the objects of interest are very small, such as lung nodules. One of the widely used feature extraction models for detecting small objects is the VGG16 network. The model, which has a small kernel of $\mathbf{3}\times \mathbf{3}$ and optimal layers, can extract the features of small objects with reasonable accuracy. In this article, feature maps are created by combining the last three layers of the VGG16 network to extract features of various sizes of nodules. This study utilizes a Region Proposal Network (RPN) to compare the accuracy of the feature map created in the proposed method and the original VGG16. An RPN is a fully-convolutional network that simultaneously predicts object bounds and objectness scores at each position. RPNs are trained end-to-end to generate high-quality region proposals, which Faster R-CNN uses for detection. In this article, we select 300, 1, 000 and 2, 000 regions chosen by the RPN network for each method; then, we calculate the recall for different Intersection over Union (IoU) ratios with ground-truth boxes. The results show that the feature map of the proposed method works more optimally than the feature map of different layers of VGG16 for extracting various sizes of nodules. Also, by reducing the number of selected region proposals, the recall of the proposed method has fewer changes than other methods.
Regression of Dense Distortion Field From a Single Fingerprint Image

Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose. In this paper, we propose a rectification method where a self-reference based network is utilized to directly estimate the dense distortion field of distorted fingerprint instead of its low dimensional representation. This method can output accurate distortion fields of distorted fingerprints with various finger poses and distortion patterns. We conducted experiments on FVC2004 DB1_A, expanded Tsinghua Distorted Fingerprint database (with additional distorted fingerprints in diverse finger poses and distortion patterns) and a latent fingerprint database. Experimental results demonstrate that our proposed method achieves the state-of-the-art rectification performance in terms of distortion field estimation and rectified fingerprint matching.
Automatic wheat ear counting in-field conditions: simulation and implication of lower resolution images

The number of ears per unit ground area, or ear density, is in most cases the main agronomic yield component of wheat. A fast evaluation of this attribute may contribute to crop monitoring and improve the efficiency of crop management practices as well as breeding programs. Currently, the number of ears is counted manually, which is time consuming. This work uses zenithal RGB images taken from above the crop canopy in natural light and field conditions. Wheat trials were carried out in two sites (Aranjuez and Valladolid, Spain) during the 2014/2015 crop season. A set of 24 varieties of durum wheat in two growing conditions with three dates of measurement were used to create the image database. The algorithm for ear counting uses three steps: (i) Laplacian frequency filter (ii) median filter (iii) Find Maxima. Although the image database was collected at the ground level, we have simulated images at lower resolutions in order to test potential application from cameras with lower resolution, such mobiles phones, action cameras (5 – 12 megapixels), or even aerial platforms (e.g. UAV from 25-50 meters). Images were resized to five different resolutions with no interpolation techniques applied. The results demonstrate high accuracy between the algorithm counts and the manual (image-based) ear counts, higher than 90% in success rate, with a decrease of <1% when images were reduced to a half of its original size, and success rates decreasing by 2.29%, 7.32%, 17.32% and 38.82% for images resized by four, eight, 16 and 32 values, respectively.
Blind Source Separation Model Research Based on Complete Ensamble Empirical Mode Decomposition with Assistant Noise

In this article describes the model of blind source separation (BSS) method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) is proposed. The influence of the signal to interference ratio (SIR) on the output error rate is investigated. Results of BSS are received using different ICA algorithms. Model under research has three stages: firstly, CEEMDAN is used for single data set decomposition into a finite and small number of intrinsic mode functions (IMF); secondly, the obtained IMFs are combined into a multidimensional signal, its correlation matrix is solved, and then the matrix eigenvalues are obtained by singular value decomposition (SVD) and used to estimate the source number; finally, selected IMFs, which correlation coefficient with observation signal is bigger, and observation signals are treated as new observation sources for ICA methods