Miracle-dz/UniFAR-FTUs huggingface dataset:
“We’re Away From Everything”: Understanding the Struggles Faced by Internationalized Schools in Non-Urban Contexts in China

This paper reports on an emerging type of international school, which we refer to as the Chinese internationalized school. This school caters to Chinese citizens and aspires to offer a fusion of national and international curricula. The majority of internationalized schools in China are to be found in large metropolitan centers. Accordingly, the literature has focused on relatively affluent urban centers, such as Shanghai, Suzhou, and Beijing. However, in recent years, internationalized schools have started to emerge outside of metropolitan areas in what have been described as tier-3 and 4 cities, which could also be described as non-urban or rural contexts, due to their remote location and proximity from metropolitan centers. This study adds to the scholarship on internationalized schooling in China by focusing on the struggles that schools in a non-urban contexts are likely to face. Focusing on one school, our interviews with 16 teachers and the school’s principal revealed that the school’s remote location made recruiting students and teachers problematic. Moreover, the effects of COVID-19 compounded this issue, with many prospective students abandoning their plans to study abroad and instead choosing to study in a local school offering the Chinese national curriculum. As a result, the school was forced to recruit students who did not possess the necessary English language ability to access the content of the Cambridge curriculum. This created a washback effect, which impacted on students’ academic achievement, their motivation, and the teaching strategies employed by the teachers. The paper concludes by considering what implications the findings might have for teachers and school leaders in internationalized schools in non-urban contexts.
Ultra-Dilute Gas of Polarons in a Bose–Einstein Condensate

We investigate the properties of a dilute gas of impurities embedded in an ultracold gas of bosons that forms a Bose–Einstein condensate (BEC). This work focuses mainly on the equation of state (EoS) of the impurity gas at zero temperature and the induced interaction between impurities mediated by the host bath. We use perturbative field-theory approaches, such as Hugenholtz–Pines formalism, in the weakly interacting regime. In turn, for strong interactions, we aim at non-perturbative techniques such as quantum–Monte Carlo (QMC) methods. Our findings agree with experimental observations for an ultra dilute gas of impurities, modeled in the framework of the single impurity problem; however, as the density of impurities increases, systematic deviations are displayed with respect to the one-body Bose polaron problem.
Liu regression after random forest for prediction and modeling in high dimension

In the modern era, using advanced technology, we have access to data with many features, and therefore, feature engineering has become a vital task in data analysis. One of the challenges in model estimation is to combat multicollinearity in high‐dimensional data problems where the number of features ( p ) exceeds the number of samples n . We propose a novel, yet simple, strategy to estimate the regression parameters in a high‐dimensional regime in the presence of multicollinearity. The proposed approach enjoys the good properties of the random forest and the simple structure of a class of linear unified estimators. We give a fast and straightforward algorithm to estimate the regression coefficients when p>n and multicollinearity exist. Numerical investigation reveals the superior performance of the method in test mean squared error. The technique is also applied to melting chemical data, where we conducted an estimation among 4885 features and discussed advantages.
Buckling of Flat Thin Plates under Combined Loading

This article aims to provide a quick methodology to determine the critical values of the forces applied to the central plane of a flat isotropic plate at which a change to the stable configuration of equilibrium occurs. Considering the variety of shapes, boundary conditions and loading combinations, the article does not intend to make an exhaustive presentation of the plate buckling. As an alternative, there will be presented only the most used configurations such as: rectangular flat thin plates, boundary conditions with simply supported (hinged) or clamped (fixed) edges, combined loadings with single compression or single shear or combination between them, compression and shear, with or without transverse loading, encountered at wings and control surfaces shell of fin and rudder or stabilizer and elevator. The reserve factor and the critical stresses will be calculated using comparatively two methods, namely the methodology proposed by the present article and ASSIST 6.6. 2.0 - AIRBUS France software, a dedicated software to local calculations, for a simply supported plate under combined loading, compression on the both sides and shear. Key Works: buckling, thin plate, simply supported, hinged edge, clamped, fixed edge, combined loading, reserve factor, ASSIST
Associations between Coronavirus Crisis Perception, Perceived Economic Risk of Coronavirus, General Self-Efficacy, and Coronavirus Anxiety at the Start of the Pandemic: Differences by Gender and Race

The coronavirus pandemic has escalated rates of anxiety in the general U.S. population. Understanding how factors associated with coronavirus anxiety at the start of the pandemic differed among populations hardest impacted by coronavirus anxiety is key to effectively remediating negatively associated health outcomes and to better understand how to address concerns of the public at the start of a global pandemic. This study was a secondary analysis of data from a cross-sectional online survey of 1165 Prolific users between 13 and 15 March 2020. Data were collected from a stratified sample of U.S. adults aged 20 or older and currently living in the United States. The sample was stratified for age, gender, and race. Coronavirus anxiety was assessed as the dependent variable, alongside three independent variables: coronavirus crisis perception, perceived economic risk of coronavirus, and general self-efficacy. Multiple linear regression assessed the associations between the independent variables and coronavirus anxiety. Interactions between independent variables and two sociodemographic variables (i.e., gender, race) were also explored. The models were adjusted for age, gender, race, education, employment, and income. The average age of participants was 45.6 ± 15.7. The majority (76%) identified as White, approximately half identified as female and reported obtaining a bachelor’s degree or higher. Coronavirus crisis perception and perceived economic risk of coronavirus were positively associated with coronavirus anxiety (β = 0.46, 95% CI = 0.41, 1.00; β = 0.14, 95% CI = 0.09, 1.00, respectively). General self-efficacy was negatively associated with coronavirus anxiety (β = −0.15, 95% CI = −1.00, −0.11). Gender and race both moderated the association between coronavirus crisis perception and anxiety. Race moderated the association between perceived economic risk and coronavirus crisis perception. These results provide a foundation to further explore cognitive factors in subgroups disproportionately affected by anxiety during the pandemic.
DATA MINING TREND IN PAST, CURRENT AND FUTURE

Data and Information or Knowledge has a significant role on human activities. Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information. Due to the importance of extracting knowledge/information from the large data repositories, data mining has become an essential component in various fields of human life including business, education, medical, scientific etc. Hence, this paper discusses the various improvements in the field of data mining from past to the present and explores the future trends.
A Fourth-Order Compact Finite Difference Scheme for Solving the Time Fractional Carbon Nanotubes Model

In this work, we deal with unsteady magnetohydrodynamic allowed convection inflow of blood with a carbon nanotubes model; the single and multiwalled carbon nanotubes of human blood are used as a based fluid. Two numerical methods used to study this model are the weighted average finite difference method and the nonstandard compact finite difference method. The proportional Caputo hybrid operator has been used to fractionalize the proposed model. Stability analysis has been construed by a kind of John von Neumann stability analysis. Numerical results are presented in diverse graphs, which manifest that the method is successful in solving the proposed model.
A computer aided plant leaf classification based on optimal feature selection and enhanced recurrent neural network

ABSTRACT This paper develops a new plant leaf classification model based on enhanced segmentation and optimal feature selection. The first process is the pre-processing, in which RGB to grey-scale conversion, histogram equalisation, and median filtering are adopted. Further, the optimised U-Net model is used for the leaf segmentation. Once the segmentation of the leaf is done, a set of features are extracted related to shape, colour, and texture. Since the length of the feature vector seems to be high that in turn affects the network training, optimal feature selection is adopted in order to reduce data dimensionality and to build robust classification models. Here, the optimal feature selection is performed by the new hybrid algorithm, namely Crow-Electric Fish Optimization (C-EFO), which is the hybridisation of Electric Fish Optimization (EFO) and Crow Search Algorithm (CSA). Finally, the deep learning model termed as Enhanced Recurrent Neural Network (E-RNN) is used for performing the classification with the improvement based on C-EFO. From the analysis, the accuracy of the proposed C-EFO+Opt-U-Net+E-RNN is 4.7% better than k-NN, 3.5% better than VGG16, 3.5% better than LSTM, and 2.75% better than RNN, respectively. Finally, the experimental results on two plant leaf databases show that the proposed method is quite effective and feasible when compared to conventional models.
How to Assess the Measurement Performance of Mobile/Wearable Point-of-Care Testing Devices? A Systematic Review Addressing Sweat Analysis

Recent advances in technologies for biosensor integration in mobile or wearable devices have highlighted the need for the definition of proper validation procedures and technical standards that enable testing, verification and validation of the overall performance of these solutions. Thus, reliable assessment—in terms of limits of detection/quantitation, linearity, range, analytical and diagnostic sensitivity/specificity, accuracy, repeatability, reproducibility, cross-reactivity, diagnostic efficiency, and positive/negative prediction—still represents the most critical and challenging aspect required to progress beyond the status of feasibility studies. Considering this picture, this work aims to review and discuss the literature referring to the available methods and criteria reported in the assessment of the performance of point-of-care testing (PoCT) devices within their specific applications. In particular, without losing generality, we focused on mobile or wearable systems able to analyze human sweat. In performing this review, the focus was on the main challenges and trends underlined in the literature, in order to provide specific hints that can be used to set shared procedures and improve the overall reliability of the identified solutions, addressing the importance of sample management, the sensing components, and the electronics. This review can contribute to supporting an effective validation of mobile or wearable PoCT devices and thus to spreading the use of reliable approaches outside hospitals and clinical laboratories.
Wind and fire: Rapid shifts in tree community composition following multiple disturbances in the southern boreal forest

Under a warming climate, the southern boreal forest of North America is expected to see a doubling in fire frequency and potential for increased wind disturbance over the next century. Although boreal forests are often considered fire-adapted, projected increases in disturbance frequency will likely result in novel combinations of disturbances with severities and impacts on community composition outside historic norms. Using a network of repeatedly measured vegetation monitoring plots, we followed changes in tree community composition in areas of the Boundary Waters Canoe Area Wilderness (BWCAW), in Minnesota, USA, experiencing disturbances ranging from severe windstorms or wildfires to areas affected by wind followed by fire or multiple fires within a short period of time. Using nonmetric multidimensional scaling ordination, hierarchical cluster analysis, and permutational analysis of variance, we compared successional pathways across different disturbance types and combinations to test whether multiple disturbances had altered successional pathways or caused greater convergence relative to single disturbances. We found that multiple disturbances often resulted in strong shifts toward wind-dispersed early-successional tree species, while single disturbances tended to have multiple successional pathways that favored both late- and early-successional species. All disturbances in our study resulted in significant shifts in composition, but we generally failed to find statistical evidence of changes in community dispersion. Although boreal forests appear to be somewhat resilient to multiple disturbance events, multiple disturbances resulted in post-disturbance tree communities that were heavily dominated by disturbance-adapted deciduous trees at the expense of conifers. Our results demonstrate that multiple disturbances are capable of altering successional pathways relative to single disturbance events and that increasingly frequent disturbances are likely to alter boreal forest structure and composition, per-haps leading to a forest region strikingly unlike that of today.
Use of ultrasonic dual-mode mixing for graphene infusion to make hybrid GFRPs: Study on mechanical performance

Optimized process parameters of a top–down approach via the ultrasonic dual-mode mixing process are adopted to disperse and exfoliate graphene nanoplatelets uniformly in the epoxy resin and fabricate hybrid GFRP laminates. The in-plane tensile and out-of-plane flexural behavior of the hybrid GFRPs made with different concentration of GNPs have been investigated. The test results show that ∼39.47% and ∼40.87% enhancement in tensile strength and flexural strength has been attained for the hybrid GFRPs made with 1 wt% of GNPs. In addition, the enhancement of ∼117% in absorbed failure energy and ∼28.56% in work of fracture is also achieved. Mori Tanaka Method has been used to calculate the modulus of the GNP-epoxy composite system, and these results are used further to predict the modulus of the hybrid GFRPs. The theoretical moduli so obtained are used to compare the experimental modulus of the hybrid GFRPs as a function of GNP concentration. Fractography examination of the fractured hybrid GFRPs reveals it reduced interlaminar delamination, which is governed by the effective load transferability of GNP-infused epoxy network.
Statistical Estimation of Turbulent Trailing Edge Noise

A new computational aeroacoustic model suitable for the prediction of turbulent airfoil trailing edge noise is presented. The method (known as the RANS based Statistical Noise Model or RSNM) combines numerically generated, mean turbulence information with a Green’s function for a semi-infinite half-plane to generate a far-field acoustic auto-spectrum. As a preliminary application of the method, the acoustic spectrum created by turbulent flow past the sharp trailing edge of a flat plate at a Reynolds number ( Re) of 5.5 × 10 5 is calculated. Four different turbulence models are used to provide the required mean flow data which is used with RSNM. The predicted spectra are shown to agree well with a semi-empirical model from the literature.
Suppression of Disk Flutter by Squeeze Air Bearing.

Due to the need for an increase in rotational speed of hard disk spindles, disk flutter has become one of the largest disturbance for the head positioning servomechanism in high track density recording. As means of suppressing disk flutter, we propose a squeeze air bearing plate, which is positioned close to the rotating disk, and investigate the flutter-suppressing effect of the bearing both experimentally and theoretically. It was found that the squeeze air bearing plate with a radial width of 15 mm, angular length of 90 degrees, and bearing gap of 170 microns can decrease the disk flutter near the leading edge of the bearing to less than one third of the original disk flutter.
Signal-to-Data Translation Model for Robust Backscatter Communications

Backscatter communication is a promising technology in the hyper-connected era. Because of its ultra-low energy consumption, it can be used in various applications, but there are performance issues due to high uncertainty. We propose a signal-to-data translation model that can transform an entire backscatter signal into the original data. To train the translation model, we developed an automation framework that can efficiently collect datasets.We also proposed a data augmentation technique suitable for backscatter signals. In extensive experiments, our model significantly outperformed a simple rule-based decoding method and a commercial RFID reader. The proposed model showed consistent performance gains across different locations, obstacles, and mobility scenarios indicating a good generalization of learning.
Risk assessment of main electrical connection in Substation with regional grid safety constraints

To ensure the safe and stable operation of the power grid, it is necessary to carry out the risk assessment of the main electrical connection in the substation. In traditional methods, the influences of the regional power grid on the main electrical connection scheme are ignored. This paper aims to propose a new method of main electrical connection risk assessment considering the regional grid safety constraints. Firstly, the traditional risk indexes are modified by considering the load shedding in the regional grid, and safety constraints indexes are built by using severity functions. Secondly, the analytic method is adopted to analyze the faults in the substation and determine the consequences, and the external equivalent of the substation is conducted. Finally, the regional grid’s power flow and optimal scheduling are calculated, and the risk assessment results are obtained. To verify the effectiveness of the proposed method, the IEEE-RTBS system and a real 220kV substation and its adjacent grid are taken for analysis. The results show that compared with the traditional method, the risk indexes obtained by the proposed method are more conservative, and the weakness of the main electrical connection under maintenance conditions can be obtained, which proves the proposed method’s feasibility and effectiveness.
Covid-19 Pandemic Crisis Affects The Emotional Intelligence and Psychological Well-Being of Lppkn Counseling Clients

It is often heard that mental health issues have a detrimental effect on one's life. If ignored, this issue will become more acute and severe in society. They are following this situation, whether the counseling services offered at the National level can efficiently address this mental health issue. Therefore, this study evaluated the relationship between emotional intelligence and psychological well-being among counseling clients in LPPKN Pahang, Malaysia. The purposive sampling technique was used to collect 143 counseling clients to answer the questionnaire. SmartPLS software was used to psychometrically evaluate the questionnaire form's data content and test the research hypotheses. The analysis results yielded several significant findings: first, there was a positive and significant relationship between self-awareness and psychological well-being. Second, there is a positive and significant relationship between emotional regulation and psychological well-being. Third, there is a positive and significant relationship between self-motivation and psychological wellbeing. Fourth, there is a positive and significant relationship between social awareness and psychological well-being. Fifth, there is a positive and significant relationship between social skills and psychological well-being. The findings of this study confirm that emotional intelligence is an essential determinant of psychological well-being. These findings are expected to assist practitioners in understanding the diversity of scopes and perspectives related to emotional intelligence and how it continues with the psychological well-being of a counseling client. Next, the best management plan and action can be planned in detail to help clients maintain their emotions and well-being in the face of life crises.
Autorotation of an Unmanned Helicopter by a Reinforcement Learning Algorithm

Autorotation maneuver requires time-critical control inputs and includes highly nonlinear dynamics. Reinforcement learning is a feasible approach that can be applied to this problem. The Q-learning is selected as a reinforcement learning algorithm and a radial basis function (RBF) network is applied as a function approximation technique to handle a large state space in the autorotation problem. Weights of RBF are updated by a back-propagation technique using a direct gradient method. The proposed reinforcement learning algorithm is evaluated by simulations based on a point-mass model of a modified OH-58A helicopter. Nomenclature T = thrust D = drag W = weight of the helicopter V = velocity of the helicopter u = horizontal velocity w = sink rate θ = angle which V makes with the horizon α = angle between thrust vector and vertical axis TPP = rotor tip path plane h = altitude Ω = angular rate of the rotor blades
Prevalence and patterns of facemask use in marketplaces in Addis Ababa: Implications for targeted SARS-CoV-2 risk communication

Abstract COVID-19 is arguably the most challenging global pandemic ever, but it is considered preventable. A number of prevention strategies have been popularized through risk communication strategies. These have included facemask wearing, which has included a lot of attention that has covered concerns including whether people wear them and how correctly. The study intended to measure the facemask-wearing practices of Addis Ababa residents in street markets as well as the factors predicting facemask use. Based on WHO protocols, the categories of facemask use included full compliance, partial use, and no facemask use across gender and age categories. Visual-epidemiology methods were used in the objective observation of facemask–wearing. Data were collected as video recordings of facemask wearing behaviors among people in street markets in a randomly selected borough of Addis Ababa by a trained cameraman over 3 days and analyzed by two coders. Individuals observed were assessed for full compliance, partial compliance, and no compliance. Coders A and B rated 316 observations individually, respectively. Inter-rater agreement averaged 91%. The observations were further analyzed using multinomial logistic regression methods. The results showed that 400 (63.3%) did not wear a facemask at all, 137 (21.7%) used facemasks correctly, and the remaining 95 (15%) used them partially. About 69% of males and 52% of females failed to use any facemask. Failure to use facemasks was highest among teens (93.6%). Further, parameter estimates showed age and gender were significant predictors of facemask use. Study limitations and implications for targeted risk communication are discussed.