Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Characteristic Model-Based Principal Component Analysis in Optimization of Flowmeter Parameters

In complex industrial processes, it is necessary to perform modeling analysis on some industrial systems and find and optimize the factors that have the greatest impact on the results, in order to achieve the optimization of the industrial systems. However, due to the high-level nature or complex working mechanism of complex industrial systems, traditional principal component analysis methods are difficult to apply. Therefore, this paper proposes a characteristic model-based principal component analysis (CMPCA) to perform principal component analysis on complex industrial systems. The differential pressure flowmeter is taken as an example to verify the effectiveness of the method. Flowmeter is an indispensable instrument in measurement, and its accuracy depends on its own structural parameters. However, the measurement accuracy of some flow meters is not high, and the measurement error is large, which affects the normal industrial production process. This method is used to analyze the influence of the structural parameters of the flowmeter on its measurement accuracy, and the four most important structural parameters are found and optimized. The measurement error of the Bitoba flowmeter is reduced from 1% to 0.2%, and the measurement repeatability is reduced from 0.3 to 0.06, which proves the effectiveness of the method.
The Fusion and Construction Strategy of Smart Sports and Traditional Sports Teaching Mode in College and Universities

Use expert interviews, literature and other methods to summarize and analyze the pros and cons of smart sports and traditional sports, combine traditional sports with smart education, and create a more scientific and effective smart education model for colleges and universities, so as to promote the joint participation or supervision of schools, teachers, parents, and students to improve the quality of physical education, so as to achieve physical education for students and enhance the time and intensity of students’ physical fitness.
A Corruption Course through a Culturally Relevant Pedagogy: The Need for an Assessment That Fits

The present study examines the challenges and the rewards of assessing learning in a seminar on corruption which is taught in a country (Iraq) where political corruption is seen as the main source of structural instability and sectarian tensions. It specifically focuses on the role of culturally relevant pedagogy in defining the most suitable summative assessment. To this end, essay test questions were developed concerning the relevance of political corruption, the impact of investigative journalism, the role of humor in investigative journalism, and the appropriate remedies (besides journalism) to political corruption. In students’ responses, investigative journalism (with or without humor) was consistently reported to be a dangerous undertaking, but disagreement emerged on its effectiveness as one of the possible remedies for corruption. Dishonesty was seen as the universal cancer of political systems and thus difficult to extirpate, albeit some systems (e.g., Al-Muhasasa) were seen as tolerating it more than others. In our study, the qualitative examination of students’ responses served three objectives: (a) it offered evidence about the extent to which culturally relevant pedagogy was realized in the course; (b) it served to put forth a proposal on how students’ test responses can inform teaching and assessment in future offerings of the course; and more broadly, (c) it synopsized the views of a sample of college students who represent the population upon whom the country heavily relies for its economic and political recovery.
Fluid–Structure Interaction and Non-Fourier Effects in Coupled Electro-Thermo-Mechanical Models for Cardiac Ablation

In this study, a fully coupled electro-thermo-mechanical model of radiofrequency (RF)-assisted cardiac ablation has been developed, incorporating fluid–structure interaction, thermal relaxation time effects and porous media approach. A non-Fourier based bio-heat transfer model has been used for predicting the temperature distribution and ablation zone during the cardiac ablation. The blood has been modeled as a Newtonian fluid and the velocity fields are obtained utilizing the Navier–Stokes equations. The thermal stresses induced due to the heating of the cardiac tissue have also been accounted. Parametric studies have been conducted to investigate the effect of cardiac tissue porosity, thermal relaxation time effects, electrode insertion depths and orientations on the treatment outcomes of the cardiac ablation. The results are presented in terms of predicted temperature distributions and ablation volumes for different cases of interest utilizing a finite element based COMSOL Multiphysics software. It has been found that electrode insertion depth and orientation has a significant effect on the treatment outcomes of cardiac ablation. Further, porosity of cardiac tissue also plays an important role in the prediction of temperature distribution and ablation volume during RF-assisted cardiac ablation. Moreover, thermal relaxation times only affect the treatment outcomes for shorter treatment times of less than 30 s.
Understanding Top‐of‐Atmosphere Flux Bias in the AeroCom Phase III Models: A Clear‐Sky Perspective

Biases in aerosol optical depths (AOD) and land surface albedos in the AeroCom models are manifested in the top‐of‐atmosphere (TOA) clear‐sky reflected shortwave (SW) fluxes. Biases in the SW fluxes from AeroCom models are quantitatively related to biases in AOD and land surface albedo by using their radiative kernels. Over ocean, AOD contributes about 25% to the 60° S– 60° N mean SW flux bias for the multi‐model mean (MMM) result. Over land, AOD and land surface albedo contribute about 40% and 30%, respectively, to the 60° S– 60° N mean SW flux bias for the MMM result. Furthermore, the spatial patterns of the SW flux biases derived from the radiative kernels are very similar to those between models and CERES observation, with the correlation coefficient of 0.6 over ocean and 0.76 over land for MMM using data of 2010. Satellite data used in this evaluation are derived independently from each other, consistencies in their bias patterns when compared with model simulations suggest that these patterns are robust. This highlights the importance of evaluating related variables in a synergistic manner to provide an unambiguous assessment of the models, as results from single parameter assessments are often confounded by measurement uncertainty. Model biases in land surface albedos can and must be corrected to accurately calculate TOA flux. We also compare the AOD trend from three models with the observation‐based counterpart. These models reproduce all notable trends in AOD except the decreasing trend over eastern China and the adjacent oceanic regions due to limitations in the emission data set.
Unpacking the Status-Leveling Burden for Women in Male-Dominated Occupations

The challenges faced by women in male-dominated occupations are often attributed to the men in, and masculine cultures of, these occupations—and sometimes to senior women in these occupations who may fail to give a “leg up” to the women coming up behind them. As such, prior research has largely focused on challenges that women experience from those of higher or equal status within the occupation and on the negative climate that surrounds women in these positions. We introduce a novel challenge, the status-leveling burden, which is the pressure put on women in male-dominated occupations from women in occupations lower in the institutional hierarchy to be their equal. Drawing on interviews with 45 surgeons, we present a model that unpacks this status-leveling burden. Our research makes novel contributions to the literatures on challenges to women in male-dominated occupations and on shared demography in cross-occupational collaboration, and it suggests new avenues for research at the intersection of gender and occupational status in the workplace.
Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning

Wind energy is a form of renewable energy with the highest installed capacity. However, it is necessary to reduce the operation and maintenance costs and extend the lifetime of wind turbines to make wind energy more competitive. This paper presents a power-derating-based Fault-Tolerant Control (FTC) model in 2 MW three-bladed wind turbines implemented using the National Renewable Energy Laboratory’s (NREL) Fatigue, Aerodynamics, Structures, and Turbulence (FAST) wind turbine simulator. This control strategy is potentially supported by the health status of the gearbox, which was predicted by means of algorithms and quantified in an indicator denominated as a merge developed by SMARTIVE, a pioneering of in this idea. Fuzzy logic was employed in order to decide whether to down-regulate the output power or not, and to which level to adjust to the needs of the turbines. Simulation results demonstrated that a reduction in the power output resulted in a safer operation, since the stresses withstood by the blades and tower significantly decreased. Moreover, the results supported empirically that a diminution in the generator torque and speed was acheived, resulting in a drop in the gearbox bearing and oil temperatures. By implementing this power-derating FTC, the downtime due to failure stops could be controlled, and thus the power production noticeably grew. It has been estimated that more than 325,000 tons of CO2 could be avoided yearly if implemented globally.
Trend Analysis of Rainfall Using Gridded Data over a Region of Southern Italy

Climate change is affecting all regions worldwide. Globally, polar ice shields are melting and the sea is rising. Moreover, some regions are facing more common extreme weather events and rainfall, while others are experiencing more extreme heat waves and droughts, causing changes in mean renewable water supplies e.g., precipitation and runoff. In this work, in order to detect possible rainfall trends in the Calabria region (southern Italy), a gridded database has been obtained from a rainfall data set of 129 monthly series collected for the period 1951–2016. In particular, the Inverse Distance Weighed was applied to build 603 rainfall grid series with a spatial resolution of 5 km × 5 km and, for each grid point, the monthly, seasonal and annual rainfall series were analyzed with the Mann–Kendall non-parametric test and the Theil–Sen estimator. Results showed a decreasing trend for the annual and winter–autumn rainfall and an increasing trend for the summer one.
Volumetric Fetal Flow Imaging With Magnetic Resonance Imaging

Fetal development relies on a complex circulatory network. Accurate assessment of flow distribution is important for understanding pathologies and potential therapies. In this paper, we demonstrate a method for volumetric imaging of fetal flow with magnetic resonance imaging (MRI). Fetal MRI faces challenges: small vascular structures, unpredictable motion, and inadequate traditional cardiac gating methods. Here, orthogonal multislice stacks are acquired with accelerated multidimensional radial phase contrast (PC) MRI. Slices are reconstructed into flow sensitive time-series images with motion correction and image-based cardiac gating. They are then combined into a dynamic volume using slice-to-volume reconstruction (SVR) while resolving interslice spatiotemporal coregistration. Compared to prior methods, this approach achieves higher spatiotemporal resolution ( $1\times 1\times 1$ mm3, ~30 ms) with reduced scan time – important features for the quantification of flow through small fetal structures. Validation is demonstrated in adults by comparing SVR with 4D radial PCMRI (flow bias and limits of agreement: −1.1 ml/s and [−11.8 9.6] ml/s). Feasibility is demonstrated in late gestation fetuses by comparing SVR with 2D Cartesian PCMRI (flow bias and limits of agreement: −0.9 ml/min/kg and [−39.7 37.8] ml/min/kg). With SVR, we demonstrate complex flow pathways (such as parallel flow streams in the proximal inferior vena cava, preferential shunting of blood from the ductus venosus into the left atrium, and blood from the brain leaving the heart through the main pulmonary artery) for the first time in human fetal circulation. This method allows for comprehensive evaluation of the fetal circulation and enables future studies of fetal physiology.
Anti-feminism, gender and the far-right gap in C/PVE measures

ABSTRACT Due to an overwhelming focus on Islamist extremism, western strategies to Counter or Prevent Violent Extremism (C/PVE) have largely neglected the growing far-right threat. In this article, we draw attention to the gender blind spot in C/PVE strategies by arguing that misogyny and masculinism go beyond ‘anti-women’ sentiment and align with the far-right’s valorisation of order, hierarchy and traditional values. This blind spot in C/PVE measures has significance for understanding the current limitations of tackling violent extremism and the disconnection between misogyny, masculinism and how we apprehend violent extremism. Therefore, a gender lens must be adopted to understand the nature of far-right extremism and such views within wider societal contexts. We examine the recent C/PVE strategies of select western states to show that they rarely connect far-right ideology and gender, and that gender is mostly represented in terms of women and role type. Australia serves as a case study based on its overt masculinism, where attitudes towards women and misogynistic violence underscore broader political and societal debates which can feed the growth of the far-right, especially when focused on the aspects of masculinism that the far-right shares with mainstream politics.
Comparison of choroidal hyperreflective spots on optical coherence tomography images between both eyes of normal subjects.

Background Advancement of optical coherence tomography (OCT) technology allows for better in vivo visualization of the choroidal architecture, which comprises vessels and stroma. However, most OCT studies using image binarization methods have focused only on choroidal vessels represented by dark pixels. This study aimed to compare the distribution of choroidal hyperreflective spots on swept-source OCT (SS-OCT) images between both eyes of normal subjects. Methods In this observational comparative study, we included SS-OCT images of healthy subjects, which were prospectively obtained to compare images among the devices. SS-OCT images acquired using PLEX Elite 9000 and DRI-OCT Triton were analyzed. En-face OCT images were obtained at five different depth positions of the inner choroid at the macula. The mean reflectivity of the choroidal slabs, the number, total area, and circularity of hyperreflective spots were quantitatively compared between the devices and between both eyes of the same subjects. Results In 30 eyes of 15 healthy subjects, the mean reflectivity of the choroidal slabs varied with the scan depth on both devices (P<0.001 and P<0.001). Hyperreflective spots were similarly distributed in the images from both devices, but at different depths. The number and area of hyperreflective spots in the second and third layers of the DRI-OCT Triton were positively correlated with those in the fourth and fifth layers of the PLEX Elite 9000, respectively (all P<0.05). The intraclass correlation coefficients (ICC) for the area of hyperreflective spots were excellent for the third slab of the DRI-OCT and the fifth slab of the PLEX Elite (ICC =0.798; 95% CI, -0.576-0.904). The number and area of hyperreflective spots were correlated between both eyes in the third, fourth and fifth layers on DRI-OCT Triton (all P<0.05) and in the first, second, and fifth layers on PLEX Elite 9000 (all P<0.05). Conclusions Hyperreflective spots in en-face images from two different SS-OCT devices were similarly observed between both eyes of the same person. The distributions of spots between the two eyes of the same person were correlated. These findings suggest that the distribution of hyperreflective spots on the choroid reflects the choroidal characteristics of the subject.
Time-Constrained Leader-Follower Multiagent Task Scheduling and Control Synthesis

This article addresses the problem of time-constrained leader-follower multiagent task scheduling and control synthesis. The leader-follower multiagent system is subject to a set of dynamically activated tasks, each of which is associated with a relative deadline and can be completed at different quality-of-satisfaction (QoSa) levels. By taking into account the reward and cost of satisfying these tasks, a novel scheduling problem is formulated and a dynamic scheduling strategy is proposed. Based on the dynamic plan, distributed control laws are designed accordingly for the leader and follower agents. Under the condition that the information of the target regions is available only to the leader agents, it is shown that the proposed control laws guarantee the satisfaction of each task at its desired QoSa level. A simulation example is given to verify the theoretical results.
Students’ prejudice as a teaching challenge: How European history educators deal with controversial and sensitive issues in a climate of political polarization

ABSTRACT Growing polarization in European societies has changed not only political landscapes but also public debates about the past, which has, in turn, had an impact on the way history is taught and talked about in schools. This article explores how these trends are experienced by history educators across Europe and asks which issues history educators perceive to be controversial and sensitive and how they approach the prejudices they frequently encounter when addressing controversial and sensitive issues. Based on focus group discussions with 33 participants from 25 European countries, the article identifies three different forms of prejudice and describes how the focus group participants tried to undermine nationalism, counter xenophobia and fight anti-Semitism. Interpreted from a practice theory perspective, the examples shared by the focus group participants indicate that teaching strategies from the disciplinary approach “toolbox” can help to undermine nationalist stereotypical thinking if they are fine-tuned to the interests and emotions of the learning group and that history teaching routines shaped by the disciplinary approach need to be interrupted on occasion in order to reflect on and deconstruct xenophobic or anti-Semitic prejudice. Overall, the article argues that history teachers need to be aware of the affective dimension of historical learning, including underlying or open prejudices held by students, in order to support the teaching of democratic education goals such as respect and openness to cultural otherness.
Cloud Detection Using an Ensemble of Pixel-Based Machine Learning Models Incorporating Unsupervised Classification

Remote sensing imagery, such as that provided by the United States Geological Survey (USGS) Landsat satellites, has been widely used to study environmental protection, hazard analysis, and urban planning for decades. Clouds are a constant challenge for such imagery and, if not handled correctly, can cause a variety of issues for a wide range of remote sensing analyses. Typically, cloud mask algorithms use the entire image; in this study we present an ensemble of different pixel-based approaches to cloud pixel modeling. Based on four training subsets with a selection of different input features, 12 machine learning models were created. We evaluated these models using the cropped LC8-Biome cloud validation dataset. As a comparison, Fmask was also applied to the cropped scene Biome dataset. One goal of this research is to explore a machine learning modeling approach that uses as small a training data sample as possible but still provides an accurate model. Overall, the model trained on the sample subset (1.3% of the total training samples) that includes unsupervised Self-Organizing Map classification results as an input feature has the best performance. The approach achieves 98.57% overall accuracy, 1.18% cloud omission error, and 0.93% cloud commission error on the 88 cropped test images. By comparison to Fmask 4.0, this model improves the accuracy by 10.12% and reduces the cloud omission error by 6.39%. Furthermore, using an additional eight independent validation images that were not sampled in model training, the model trained on the second largest subset with an additional five features has the highest overall accuracy at 86.35%, with 12.48% cloud omission error and 7.96% cloud commission error. This model’s overall correctness increased by 3.26%, and the cloud omission error decreased by 1.28% compared to Fmask 4.0. The machine learning cloud classification models discussed in this paper could achieve very good performance utilizing only a small portion of the total training pixels available. We showed that a pixel-based cloud classification model, and that as each scene obviously has unique spectral characteristics, and having a small portion of example pixels from each of the sub-regions in a scene can improve the model accuracy significantly.
Seismic fragility of bridges: An approach coupling multiple-stripe analysis and Gaussian mixture for multicomponent structures

An approach is developed to build multivariate probabilistic seismic demand models (PSDMs) of multicomponent structures based on the coupling of multiple-stripe analysis and Gaussian mixture models. The proposed methodology is eminently flexible in terms of adopted assumptions, and a classic highway bridge in Eastern Canada is used to present an application of the new approach and to investigate its impact on seismic fragility analysis. Traditional PSDM methods employ lognormal distribution and linear correlation between pairs of components to fit the seismic response data, which may lead to poor statistical modeling. Using ground motion records rigorously selected for the investigated site, data are generated via response history analysis, and appropriate statistical tests are then performed to show that these hypotheses are not always valid on the response data of the case-study bridge. The clustering feature of the proposed methodology allows the construction of a multivariate PSDM with refined fitting to the correlated response data, introducing low bias into the fragility functions and mean annual frequency of violating damage states, which are crucial features for decision making in the context of performance-based seismic engineering.