Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperbolic and viscous conservation laws

Preface 1. Hyperbolic conservation laws Preliminaries Riemann problem Wave interactions Random choice method Nonlinear superposition Large-time behavior and tegularity 2. Viscous vonservation laws Preliminaries Burgers equation Diffusion waves Viscous shocks Viscous rarefaction waves Concluding remarks Bibliography Index.
Preparation and characterization of sesame peptide-calcium chelate with different molecular weight

ABSTRACT Sesame peptides could be used to treat underlying diseases and a suitable carrier of nutrients. In this study, sesame peptides were mainly prepared from SSM (sesame meal), and the chelating ability of different molecular weight sesame peptide components to calcium and the physical characterization of the chelates were investigated. After that, the molecular weights were verified by HPLC (high performance liquid chromatography), and the Zeta potential, PDI (polydispersity) of SSMP (sesame meal peptides) of different molecular weights were determined. In the SSMP-Ca chelates, the chelation rate of SSMP-2 (sesame meal peptide components 2) to calcium is 72.72 ± 0.17%. Fluorescence spectrum analysis found that the fluorescence intensity of chelates decreased, and the maximum absorption peak was red-shifted. FT-IR (Fourier Transform Infrared spectroscopy) research showed that the chelate vibration absorption wavelengths of the three characteristic peaks -NH2, COO−, and C = O had change, indicating that the amino and carboxyl groups in sesame peptide participated in the chelation reaction. SEM (scanning electron microscope) analysis showed that SSMP and calcium could form a chelate with specific stability. Most of its microstructures were encapsulated and adhered to each other to create a “bridging effect” and appear as aggregates. These results provided a basis for using sesame peptides in the food or pharmaceutical industry.
NCEP NOTES Implementation in the NCEP GFS of a Hybrid Eddy-Diffusivity Mass-Flux (EDMF) Boundary Layer Parameterization with Dissipative Heating and Modified Stable Boundary Layer Mixing

The current operational eddy-diffusivity countergradient (EDCG) planetary boundary layer (PBL) scheme in the NCEP Global Forecast System (GFS) tends to underestimate the PBL growth in the convective boundary layer (CBL). To improve CBL growth, an eddy-diffusivity mass-flux (EDMF) PBL scheme is developed, where the nonlocal transport by large turbulent eddies is represented by a mass-flux (MF) scheme and the local transport by small eddies is represented by an eddy-diffusivity (ED) scheme. For the verticalmomentummixing, theMF scheme is modified to include the effect of the updraft-induced pressure gradient force. While the EDMF scheme displays better CBL growth than the EDCG scheme, it tends to overproduce the amount of low clouds and degrades wind vector forecasts over the tropical ocean where strongly unstable PBLs are rarely found. In order not to degrade the forecast skill in the tropics, a hybrid scheme is developed, where the EDMF scheme is applied only for the strongly unstable PBL,while theEDCGscheme is used for theweakly unstable PBL.Alongwith the hybridEDMF scheme, the heating by turbulent kinetic energy (TKE) dissipation is parameterized to reduce an energy imbalance in the GFS.To enhance a tooweak vertical turbulentmixing forweakly andmoderately stable conditions, the current local scheme in the stable boundary layer (SBL) is modified to use an eddy-diffusivity profilemethod. The hybrid EDMF PBL scheme with TKE dissipative heating and modified SBL mixing led to significant improvements in some key medium-range weather forecast metrics and was operationally implemented into the NCEP GFS in January 2015.
Interactions of Isoquinoline Alkaloids with Transition Metals Iron and Copper

Data on alkaloid interactions with the physiologically important transition metals, iron and copper, are mostly lacking in the literature. However, these interactions can have important consequences in the treatment of both Alzheimer’s disease and cancer. As isoquinoline alkaloids include galanthamine, an approved drug for Alzheimer’s disease, as well as some potentially useful compounds with cytostatic potential, 28 members from this category of alkaloids were selected for a complex screening of interactions with iron and copper at four pathophysiologically relevant pH and in non-buffered conditions (dimethyl sulfoxide) by spectrophotometric methods in vitro. With the exception of the salts, all the alkaloids were able to chelate ferrous and ferric ions in non-buffered conditions, but only five of them (galanthine, glaucine, corydine, corydaline and tetrahydropalmatine) evoked some significant chelation at pH 7.5 and only the first two were also active at pH 6.8. By contrast, none of the tested alkaloids chelated cuprous or cupric ions. All the alkaloids, with the exception of the protopines, significantly reduced the ferric and cupric ions, with stronger effects on the latter. These effects were mostly dependent on the number of free aromatic hydroxyls, but not other hydroxyl groups. The most potent reductant was boldine. As most of the alkaloids chelated and reduced the ferric ions, additional experimental studies are needed to elucidate the biological relevance of these results, as chelation is expected to block reactive oxygen species formation, while reduction could have the opposite effect.
Helium Reionization Simulations. II. Signatures of Quasar Activity on the IGM

We have run a new suite of simulations that solve hydrodynamics and radiative transfer simultaneously to study helium ii reionization. Our suite of simulations employs various models for populating quasars inside of dark matter halos, which affect the He ii reionization history. In particular, we are able to explore the impact that differences in the timing and duration of reionization have on observables. We examine the thermal signature that reionization leaves on the intergalactic medium (IGM), and measure the temperature-density relation. As previous studies have shown, we confirm that the photoheating feedback from helium ii reionization raises the temperature of the IGM by several thousand kelvin. To compare against observations, we generate synthetic Lyα forest sightlines on-the-fly and match the observed effective optical depth of hydrogen to recent observations. We show that when the simulations have been normalized to have the same values of , the effect that helium ii reionization has on observations of the hydrogen Lyα forest is minimal. Specifically, the flux PDF and the one-dimensional power spectrum are sensitive to the thermal state of the IGM, but do not show direct evidence for the ionization state of helium. We show that the peak temperature of the IGM typically corresponds to the time of 90%–95% helium ionization by volume, and is a relatively robust indicator of the timing of reionization. Future observations of helium reionization from the hydrogen Lyα forest should thus focus on measuring the temperature of the IGM, especially at mean density. Detecting the peak in the IGM temperature would provide valuable information about the timing of the end of helium ii reionization.
Comparing Machine and Deep Learning Methods for the Phenology-Based Classification of Land Cover Types in the Amazon Biome Using Sentinel-1 Time Series

The state of Amapá within the Amazon biome has a high complexity of ecosystems formed by forests, savannas, seasonally flooded vegetation, mangroves, and different land uses. The present research aimed to map the vegetation from the phenological behavior of the Sentinel-1 time series, which has the advantage of not having atmospheric interference and cloud cover. Furthermore, the study compared three different sets of images (vertical–vertical co-polarization (VV) only, vertical–horizontal cross-polarization (VH) only, and both VV and VH) and different classifiers based on deep learning (long short-term memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Units (GRU), Bidirectional GRU (Bi-GRU)) and machine learning (Random Forest, Extreme Gradient Boosting (XGBoost), k-Nearest Neighbors, Support Vector Machines (SVMs), and Multilayer Perceptron). The time series englobed four years (2017–2020) with a 12-day revisit, totaling 122 images for each VV and VH polarization. The methodology presented the following steps: image pre-processing, temporal filtering using the Savitsky–Golay smoothing method, collection of samples considering 17 classes, classification using different methods and polarization datasets, and accuracy analysis. The combinations of the VV and VH pooled dataset with the Bidirectional Recurrent Neuron Networks methods led to the greatest F1 scores, Bi-GRU (93.53) and Bi-LSTM (93.29), followed by the other deep learning methods, GRU (93.30) and LSTM (93.15). Among machine learning, the two methods with the highest F1-score values were SVM (92.18) and XGBoost (91.98). Therefore, phenological variations based on long Synthetic Aperture Radar (SAR) time series allow the detailed representation of land cover/land use and water dynamics.
Prediction of Weyl semimetal in orthorhombic MoTe2

We investigate the orthorhombic phase $({T}_{d})$ of the layered transition-metal dichalcogenide ${\mathrm{MoTe}}_{2}$ as a Weyl semimetal candidate. ${\mathrm{MoTe}}_{2}$ exhibits four pairs of Weyl points lying slightly above $(\ensuremath{\sim}6\phantom{\rule{0.16em}{0ex}}\mathrm{meV})$ the Fermi energy in the bulk band structure. Different from its cousin ${\mathrm{WTe}}_{2}$, which was recently predicted to be a type-II Weyl semimetal, the spacing between each pair of Weyl points is found to be as large as 4% of the reciprocal lattice in ${\mathrm{MoTe}}_{2}$ (six times larger than that of ${\mathrm{WTe}}_{2}$). When projected onto the surface, the Weyl points are connected by Fermi arcs, which can be easily accessed by angle-resolved photoemission spectroscopy due to the large Weyl point separation. In addition, we show that the correlation effect or strain can drive ${\mathrm{MoTe}}_{2}$ from a type-II to a type-I Weyl semimetal.
IMPACT OF NEW GAMOW–TELLER STRENGTHS ON EXPLOSIVE TYPE IA SUPERNOVA NUCLEOSYNTHESIS

Recent experimental results have confirmed a possible reduction in the Gamow–Teller (GT+) strengths of pf-shell nuclei. These proton-rich nuclei are of relevance in the deflagration and explosive burning phases of SNe Ia. While prior GT strengths result in nucleosynthesis predictions with a lower-than-expected electron fraction, a reduction in the GT+ strength can result in a slightly increased electron fraction compared to previous shell model predictions, though the enhancement is not as large as previous enhancements in going from rates computed by Fuller, Fowler, and Newman based on an independent particle model. A shell model parametrization has been developed that more closely matches experimental GT strengths. The resultant electron-capture rates are used in nucleosynthesis calculations for carbon deflagration and explosion phases of SNe Ia, and the final mass fractions are compared to those obtained using more commonly used rates.
Rule-based approximation of black-box classifiers for tabular data to generate global and local explanations

The need to understand the decision bases of artificial intelligence methods is becoming widespread. One method to obtain explanations of machine learning models and their decisions is the approximation of a complex model treated as a black box by an interpretable rule-based model. Such an approach allows detailed and understandable explanations to be generated from the elementary conditions contained in the rule premises. However, there is a lack of research on the evaluation of such an approximation and the influence of the parameters of the rule-based approximator. In this work, a rule-based approximation of complex classifier for tabular data is evaluated. Moreover, it was investigated how selected measures of rule quality affect the approximation. The obtained results show what quality of approximation can be expected and indicate which measure of rule quality is worth using in such application.
Alarm Pattern Recognition in Continuous Process Control Systems using Data Mining

An alarm management system with the Human Machine Interface in a process control system is used to alert an operator of any abnormal situation, so that corrective action can be taken to ensure safety and productivity of the plant and quality of the product. An alarm system reporting many alarms even during the normal state of the plant is due to chattering alarms, duplicated alarms, intermittent equipment problems and certain alarms configured in the system which may not have any importance. In such a situation the operator may miss certain critical alarms leading to undesirable outcomes. So, to have an optimum alarm system, the unwanted alarms have to be identified and eliminated. In this paper, we propose an offline method to identify repetitive, frequent sequences or patterns using PrefixSpan and Bi-Directional Extension algorithms. With the identified sequences or patterns, plant operation experts can improve the effectiveness of the alarm system through alarm rationalization so that this will help the operator in making the plant more safe, reliable and productive. The main objectives of this work are the following: (i) to use a definitive method to represent alarm data in an alarm log which is Temporal data as Itemsets without a need for complex mathematical, statistical or visual methods; (ii) to use data mining algorithms for identifying Frequent sequences which can be implemented on a normal computing resource such as Personal computer; (iii) to apply the method to the complete alarm data available no matter how big they are; (iv) to study and establish that the chosen method is possible to be applied to larger sized datasets.
Discriminating strange star mergers from neutron star mergers by gravitational-wave measurements

We perform three-dimensional relativistic hydrodynamical simulations of the coalescence of strange stars and explore the possibility to decide on the strange matter hypothesis by means of gravitational-wave measurements. Self-binding of strange quark matter and the generally more compact stars yield features that clearly distinguish strange star from neutron star mergers, e.g. hampering tidal disruption during the plunge of quark stars. Furthermore, instead of forming dilute halo structures around the remnant as in the case of neutron star mergers, the coalescence of strange stars results in a differentially rotating hypermassive object with a sharp surface layer surrounded by a geometrically thin, clumpy high-density strange quark matter disk. We also investigate the importance of including nonzero temperature equations of state in neutron star and strange star merger simulations. In both cases we find a crucial sensitivity of the dynamics and outcome of the coalescence to thermal effects, e.g. the outer remnant structure and the delay time of the dense remnant core to black hole collapse depend on the inclusion of nonzero temperature effects. For comparing and classifying the gravitational-wave signals, we use a number of characteristic quantities like the maximum frequency during inspiral or the dominant frequency of oscillations of the postmergermore » remnant. In general, these frequencies are higher for strange star mergers. Only for particular choices of the equation of state the frequencies of neutron star and strange star mergers are similar. In such cases additional features of the gravitational-wave luminosity spectrum like the ratio of energy emitted during the inspiral phase to the energy radiated away in the postmerger stage may help to discriminate coalescence events of the different types. If such characteristic quantities could be extracted from gravitational-wave signals, for instance with the upcoming gravitational-wave detectors, a decision on the strange matter hypothesis and the existence of strange stars should be possible. « less
Parasitic Component Small-Signal Modeling and Control of a Practical CLLC Resonant Converter

This article presents a comprehensive characterization of an asymmetric resonant CLLC dc/dc converter plant transfer function obtained using a generalized harmonic approximation (GHA)-based small-signal modeling approach. The effect of circuit parasitic components is comprehensively considered while deriving this model, and a quantified comparison of the resultant plant frequency response with a conventional first harmonic approximation (FHA)-based model is presented, which provides the designer insightful findings to design a robust and noise immune closed-loop compensator. Furthermore, a thorough explanation of a sliding mode control (SMC) along with detailed parameterization of the controller coefficients is provided by analyzing the system’s dynamic behavior and comparing the response with a conventional proportional–integral (PI)-based controller. In addition to the objective of designing a robust SMC controller, a phase shift-based secondary side modulation is introduced, which facilitates a significant reduction in the secondary side switching losses, thus enhancing the steady-state efficiency of the overall system. To validate and benchmark the open-loop plant response and controller dynamics, detailed steady-state results are elucidated for a 400–28- and 400–24-V voltage conversion at a rated load of 1 kW, with a resonant frequency of 500 kHz. Furthermore, a comprehensive experimental comparison between the proposed hybrid control scheme and the conventional PI controller is shown for two dynamic load changes corresponding to 10%–90% load step-up and 90%–10% load step-down. Quantification of dynamic response portrays a settling time reduction of 46.4% and an over/undershoot reduction of 33%, thus validating the robustness of the proposed control scheme.
Crosstalk Analysis of Printed Circuit Board Traces With Right-Angled Bent Corners via Time Domain Hybrid Method

Discontinuous impedance exists when bent corners are introduced into the traces of printed circuit board (PCB). By combining transmission line (TL) equations, higher order finite-difference time-domain (FDTD) method, state variable method, and Norton's theorem, an efficient time domain hybrid method is proposed to realize the fast crosstalk simulation of the traces with right-angled bent corners (RABCs) in this work. First, the whole structure of the traces is decomposed into a cascade network, which consists of multiple equal length parallel microstrip lines (EL-PMSLs) and the RABC structures. Then, the crosstalk responses on these EL-PMSLs can be quickly solved by applying TL equations combined with the higher order FDTD method. With the application of Norton's theorem, the equivalent circuit model of whole PCB traces is constructed. Finally, the equivalent circuits of the RABC structures are extracted, and the port voltages of these RABCs are calculated via state variable method and fed back to these EL-PMSLs as boundaries, thus the round-trip transmission of interference signals on the PCB traces is realized. Numerous crosstalk simulations of PCB traces with typical RABC structures, as well as extracted from a practical PCB, are presented and compared with the results of commercial software CST to verify the correctness and efficiency of the proposed method.
Measurement of the Kerr Spin Parameter by Observation of a Compact Object's Shadow

A black hole casts a shadow as an optical appearance because of its strong gravitational field. We study how to determine the spin parameter and the inclination angle by observing the apparent shape of the shadow, which is distorted mainly by those two parameters. Defining some observables characterizing the apparent shape (its radius and distortion parameter), we find that the spin parameter and inclination angle of a Kerr black hole can be determined by the observation. This technique is also extended to the case of a Kerr naked singularity.
Development of a Combined Lipid-Based Nanoparticle Formulation for Enhanced siRNA Delivery to Vascular Endothelial Cells

Low transfection efficiency in endothelial cells (EC) is still a bottleneck for the majority of siRNA-based vascular delivery approaches. In this work, we developed a lipid-based nanoparticle (LNP) formulation based on a combination of a permanently charged cationic lipid-DOTAP and a conditionally ionized cationic lipid-MC3 (DOTAP/MC3) for the enhanced delivery of siRNA into EC. Compared with a single DOTAP or MC3-based benchmark LNP, we demonstrated that the DOTAP/MC3 LNP formulation shows the best transfection efficiency both in primary EC in vitro and in endothelium in zebrafish. The high transfection activity of the DOTAP/MC3 LNP formulation is achieved by a combination of improved endothelial association mediated by DOTAP and MC3-triggered efficient siRNA intracellular release in EC. Furthermore, AbVCAM-1-coupled DOTAP/MC3 LNP-mediated siRNARelA transfection showed pronounced anti-inflammatory effects in inflammatory-activated primary EC by effectively blocking the NF-κB pathway. In conclusion, the combination of permanent and ionizable cationic lipids in LNP formulation provides an effective endothelial cell delivery of siRNA.
Systematic review and meta-analysis of intravenous and topical tranexamic acid in reducing blood loss in knee arthroplasty

InTrodUcTIon: The purpose of this review and meta-analysis is to compare tranexamic acid (TXA) administration via the intravenous route (IV-TXA) and topical route (T-TXA), in reducing blood loss in knee arthroplasty. MATerIAL And MeTHods: A systematic literature search was performed using Medline, EMBASE, Scopus and CENTRAL databases till December 20, 2021. Outcomes of interest included blood loss, hematocrit and hemoglobin drop, and adverse events. resULTs: A total of 3,363 patients (n = 1,307 in IV-TXA group; n = 2,056 in T-TXA group) from 23 studies were included. There was no statistically significantly difference between IV-TXA and T-TXA among to: total blood loss (874.8 ± 349.7 mL vs 844.9 ± 366.6 mL, respectively; SMD = 0.13; 95% CI: −9.37 to 85.32; p = 0.15), as well as transfusion needed (10.9% vs 15.4% respectively (RR = 0.79; 95% CI: 0.60 to 1.04; p = 0.09). Blood loss from the drain in IV-TXA and T-TXA varied and occurred 377.9 ± 191.9 vs 302.9 ± 182.6 mL for IV-TXA and T-TXA, respectively: (SMD = 0.52; 95% CI: 0.02 to 1.02; p = 0.04). concLUsIons: Our clinical findings support that TXA can effectively, safely, and decrease the number of transfusions without severe side effects in patients undergoing TKA. However, given the reports from individual single clinical trials of the superiority of T-TXA, further clinical trials and meta-analyses based on these findings are needed to standardize the approach to TXA use in patients undergoing knee arthroplasty.