Miracle-dz/UniFAR-FTUs huggingface dataset:
The protective role of self-compassion on test anxiety among adolescents

ABSTRACT A growing body of work has begun to identify the benefits of self-compassion in promoting positive mental health and wellbeing for adolescents. Test anxiety is a form of anxiety elicited in response to examinations, which can negatively impact academic achievement and wellbeing. The current pilot study explored the effect of self-compassion on test anxiety among adolescents, while controlling for the effects of gender and general anxiety. Participants were adolescents (n = 47), aged 16 to 17 years old, enrolled in a post-primary school in Northern Ireland and studying for qualifications after compulsory education. Hierarchical regression analysis found that higher levels of self-compassion were related to reduced test anxiety. However, female adolescents and those with higher levels of general anxiety reported greater test anxiety. The findings highlight the potential value of self-compassion as a protective factor against test anxiety for young people in schools. The implications for practitioners supporting young people with mental health and wellbeing difficulties are considered.
AN ALL DIGITAL PHASE-LOCKED LOOP BASED ON HILBERT FILTERING

Based on Hilbert filtering an all-digital phase-locked loop (ADPLL) is designed to actualize thedigitalized measurement of the frequency and phase of lowfrequency AC signal. Firstly, after the DA conversion thesignal to be measured is inputted into a pair of all digitalHilbert phase-shifting filters and two orthogonal output signals with same amplitudes are obtained, proceeding to the next step, the phase angle of the input signal can be calculated. Then the calculated phase angle of the input signal is inputted into an all digital processing system based on PLL structure and thefrequency and phase angle of the measured signal can bemeasured and calculated. In this measuring method the phase information contained in the waveform of the input signal itself is fully utilized, so both the accuracy of the phasediscrimination for low frequency signals and the tracing speed of the locked phase are improved, and the consumed time at which the measurement process reaches its steady state can be saved. The proposed algorithm can be easily realized with DSP or other microprocessor programming codes. The presentedmethod is suitable for measuring the frequency and phase offundamental frequency of power system and the obtained data can be used in the monitoring of power system, at the same time, it can provide the phase reference to power electronic devices which should be operated synchronously.
Deep Learning Model for Enhanced Crop Identification From Landsat 8 Images

Deep learning is a powerful state-of-the-art technique for image processing, including remote sensing images. This paper describes a multilevel deep learning based crop type identification system that targets land cover and crop type classification from multi-temporal multisource satellite imagery. The proposed crop type identification is based on unsupervised neural network that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised neural networks. The main part of this multilayer deep network with Self Organizing maps and atmospheric correction is an ensemble of CNNs. The proposed system is applied for crop identification using Landsat-8 time-series and implemented with different sized vector data, parcel boundary. Aided with self-organizing maps and atmospheric correction for pre-processing doing both pixel based and parcel based analysis, this proposed crop type identification system allowed us to achieve the overall classification accuracy of nearly 95% for three different time periods.
Progress and challenges in modeling dynamics-microphysics interactions: from the Pi chamber to Monsoon convection

The CAIPEEX (Cloud Aerosol Interaction and Precipitation Enhancement Experiment) monsoon convective clouds case was designed to explore the impacts of environmental and cloud condensation nuclei (CCN) conditions on monsoon convection. Pi chamber warm cloud case The scientific objectives are 1) to demonstrate the model capability of representing the detailed microphysical processes happening in the cloud chamber and how different models behave in different aerosol injection rates, 2) to reveal the model uncertainties and limitations in the existing modeling tools, and 3) to provide guidance and recommendations for future work to improve cloud chamber simulations and model–laboratory comparisons. The comparison was performed among a diverse set of model categories, including four types of LES models (Dziekan et al. 2019;Shima et al. 2009, 2020;Niedermeier et al. 2020;Khairoutdinov and Randall 2003) performed by the University of Warsaw, University of Hyogo, Leibniz Institute for Tropospheric Research, and Brookhaven National Laboratory;two types of direct numerical simulation (DNS) models (Chen et al. 2021;Richter et al. 2021) by the National Center for Atmospheric Research (NCAR) and the University of Notre Dame;and the Linear Eddy Model (LEM;Su et al. 1998) by the University of Utah. Interestingly, however, the amount of updraft tilting was sensitive not only to the vertical wind shear used in the model, but also to the method of cloud initiation, i.e., forcing using warm bubbles or surface heat fluxes.
Further Tests of the ZCAPM Asset Pricing Model

In a recent book, Kolari et al. developed a new theoretical capital asset pricing model dubbed the ZCAPM. Based on out-of-sample cross-sectional tests using U.S. stocks, the ZCAPM consistently outperformed well-known multifactor models popular in the finance literature. This paper presents further evidence that expands their sample period from 1927 to 2020. Results are provided for the subperiods 1927 to 1964 and 1965 to 2020. Our results corroborate those of KLH. In cross-sectional tests, the ZCAPM outperforms the CAPM as well as the Fama and French three-factor model and Carhart four-factor model. Outperformance is found in terms of both higher goodness of fit and the statistical significance of factor loadings. Interestingly, the earlier subperiod results highlight problems with the endogeneity of test assets in cross-sectional tests of multifactor models.
Research on 3D MFL testing of wire rope based on empirical wavelet transform and SRCNN

Magnetic flux leakage (MFL) testing is one of the most effective methods in nondestructive testing of wire rope. However, traditional MFL testing devices have problems such as low recognition rate, single detection dimension and fuzzy magnetic leakage image. Based on the non-saturated magnetic excitation 3D MFL testing device, this paper proposes a wavelet denoising method based on empirical wavelet transform (EWT) to denoise the collected 3D MFL signal. After noise reduction, the Signal-to-Noise Ratio (SNR) and Root Mean Squared Error (RMSE) of the three dimensions have improved. Color imaging technology is used to fuse defect grayscale images into color images, and Super-Resolution Convolutional Neural Network (SRCNN) is applied to MFL images of broken wires. After SRCNN reconstruction, the resolution of defect color images is improved. The color moment feature of the defect color image is extracted as the input of the Elman neural network to quantitatively identify broken wires. Experimental results show that the noise reduction algorithm can effectively suppress the noise in three dimensions, and the broken wires recognition rate after reconstruction has been significantly improved, which verifies the effectiveness of SRCNN in wire rope MFL images.
Empirical Study On Key Attributes of Yelp dataset which Account for Susceptibility of a user to Social Influence

This thesis focuses on finding the key attributes of a social network of friends that help us to understand and measure the `Susceptibility’ of a user to `Influence’. Susceptibility of a user here refers to a factor that explains the state of being likely to get effected by a friend in a social network to perform a certain action. On the other hand, Influence refers to a force or phenomenon where a certain action performed by a user on a particular product or business impacts the activity of his friends on that same product. For this study I have selected Yelp, as it is one of the huge and active social rating network where users across the globe participate in providing meaningful reviews about various businesses. User activity here refers to rating or reviewing a business in a particular location. With such volume of data and such huge social network of friends, it makes a perfect dataset to perform our study. The common attributes that would help us to measure susceptibility in a social rating network were recognized and were used to generate a mathematical model for susceptibility. In the end, we were also able to recognize an efficient learning algorithm that fits Yelp dataset and may be used to help predict susceptibility of a user to influence. In our experiments the final susceptibility measures determined were higher when compared to a dataset model prior to considering the key attributes. With this study, we could clearly see that a review of an elite and active friend in Yelp has a significant impact on the susceptibility of a user to influence. Along with the number of reviews exposed to a user by his friends, a new statistic named “useful voting fraction” for each of their friends also helped to derive an efficient learning algorithm that fits Yelp Dataset. Finally, we also found that the star rating which each review gets, plays an important role in measuring susceptibility.
Progress in the Development of Graphene-Based Biomaterials for Tissue Engineering and Regeneration

Over the last few decades, tissue engineering has become an important technology for repairing and rebuilding damaged tissues and organs. The scaffold plays an important role and has become a hot pot in the field of tissue engineering. It has sufficient mechanical and biochemical properties and simulates the structure and function of natural tissue to promote the growth of cells inward. Therefore, graphene-based nanomaterials (GBNs), such as graphene and graphene oxide (GO), have attracted wide attention in the field of biomedical tissue engineering because of their unique structure, large specific surface area, good photo-thermal effect, pH response and broad-spectrum antibacterial properties. In this review, the structure and properties of typical GBNs are summarized, the progress made in the development of GBNs in soft tissue engineering (including skin, muscle, nerve and blood vessel) are highlighted, the challenges and prospects of the application of GBNs in soft tissue engineering have prospected.
Safe Robot Trajectory Control Using Probabilistic Movement Primitives and Control Barrier Functions

In this paper, we present a novel means of control design for probabilistic movement primitives (ProMPs). Our proposed approach makes use of control barrier functions and control Lyapunov functions defined by a ProMP distribution. Thus, a robot may move along a trajectory within the distribution while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. The control employs feedback linearization to handle nonlinearities in the system dynamics and real-time quadratic programming to ensure a solution exists that satisfies all safety constraints while minimizing control effort. Furthermore, we highlight how the proposed method may allow a designer to emphasize certain safety objectives that are more important than the others. A series of simulations and experiments demonstrate the efficacy of our approach and show it can run in real time.
Organic matter compartments in an Ultisol under integrated agricultural and livestock production systems in the Cerrado

ABSTRACT: Integrated agricultural production systems have the potential to increase organic matter content, which is reflected in the soil carbon (C) and nitrogen (N) concentrations. Here, we evaluated the C and N stocks and its compartments in a typical distro cohesive yellow Ultisol under the no-till (NT) and crop-livestock integration (CLI) systems, in eastern Maranhão. Five areas with different management strategies were evaluated, more specifically, one area was managed under the NT system in succession for 14 years (soybean/millet), three areas had different CLI system adoption histories (i.e., CLI was adopted 2, 4, or 8 years prior to sample collection); and finally, one area consisted of native Cerrado (savannah) vegetation. Soil samples were collected at depths of 0-0.10, 0.10-0.20, 0.20-0.30, and 0.30-0.50 m,to analyze the content and total stocks of carbon (C) and its compartments (physical and chemical), N content, soil microbial biomass, and basal respiration. Results revealed higher content and stock of C and N in areas with CLI. The principal humic substance reported was humin, and higher concentrations of C associated with minerals were present in areas under more recent CLI adoption (2 and 4 years). We demonstrated that the adoption of CLI, even if recent, leads to immediate increases in the concentrations of C and its fractions as a result of using forage crops. NT for 14 years and CLI for 8 years exhibited higher levels of C management and higher soil biological activity due to the greater stability of these systems.
Development of an Electronic Medical Tool to Facilitate Allocation of Limited Resources in Times of Crisis

The COVID-19 pandemic has made decisions about resource allocation and reallocation real possibilities even in high-resource settings. In April 2020, in preparation for such an eventuality, Atlantic Health System began to develop a real-time instrument built into the EMR to assist with such decisions. The instrument calculated the modified Sequential Organ Failure Assessment for all patients admitted, in real time, to assist triage teams make decisions if crisis standards of care were declared. A pilot assessment of the instrument was performed using retrospective data by nine members of the triage teams, who were asked to identify the six patients at highest risk of reallocation. Agreement about which patients were at highest risk of resource reallocation was good, but not perfect. All raters agreed on five of the six patients, but only seven of nine agreed on the final patient. Among the six consensus selections for reallocation, five died prior to hospital discharge. All patients at highest risk of reallocation had a predicted life expectancy of less than 1 year. In conclusion, the instrument was easy to use, and the concordance among raters was good but not perfect. Predicted life expectance was a major determinant of the triage score.
Changes in students' learning skills through the first-year experience course: a case study over three years at a Japanese university

PurposeThis study was conducted to understand students' achievements in learning and to improve the overall curriculum of the first-year experience course.Design/methodology/approachIn this study, a series of questionnaire-based surveys were conducted on students enrolled in the Introductory Seminar for Policy Science, a mandatory first-year experience course offered in the first semester (from April to July) at a university in Japan. The studies were conducted in 2015 (n = 29), 2016 (n = 29) and 2017 (n = 31). FindingsResults revealed that, regardless of the year, students deepened their understanding of policy science and gained increased confidence to explain what group works and reports are throughout the semester. In addition, students' level of worry about life at the university decreased throughout the course in all three years. A stepwise multiple regression analysis (n = 84) revealed that those students who knew what policy science was (B = 0.271) and had the confidence to write their opinions in reports (B = 0.264) more likely answered that they knew what they wanted to study over four years at the university.Originality/valueThis study revealed that the mandatory first-year experience course taught by the same instructor generated similar educational effects for different students in different years. The results elucidated the progressive effects of different components of the course, eliminating possibilities of any bias or specific characteristics of a single group of students.
Jesus’ parables for and by ten-year-olds: applying blending theory in in-depth analyses of pupils’ RE texts, RE textbook passages, and teacher’s handbook passages in the context of Norwegian inclusive public religious education

ABSTRACT This article presents the results of an in-depth analysis of ten texts written by ten-year-old pupils during a religious education (RE) lesson about Jesus’ parables. The texts from the pupils’ notebooks were analysed with passages from the RE textbook and the teacher’s handbook to explore how these books influenced the pupils’ texts. The main theoretical framework guiding our analysis was conceptual blending theory, which enabled us to outline how the pupils had interrelated and integrated information from different sources during their reading, understanding, and interpretation of Jesus’ parable about the lost/retrieved sheep (Luke 15:4–7). We found substantial similarities between the pupils’ texts, the RE textbook, and the teacher’s handbook, but the pupils’ texts also included information they had acquired from other sources, such as the RE lesson or their leisure activities. The emphasis on Christian academic insider perspectives in the textbook influenced the pupils’ interpretations but did not necessarily restrain them. Some pupils’ texts offer interpretations that are relatively independent, although they primarily represent Christian insider perspectives, both academic and personal. Additionally, they illuminate the internal diversity of Christianity. Our focus on empirical material complements previous research on narrative RE based on historical and educational philosophical approaches.
Milk Intake in Early Life and Later Cancer Risk: A Meta-Analysis

Dairy consumption in adulthood has been demonstrated to influence cancer risk. Although childhood and adolescence represent critical periods of rapid growth, the relationship between milk intake in early life and later cancer risk is unclear. Thus, we examined this relationship by conducting a meta-analysis of the observational studies. PubMed and Embase were searched for relevant articles that were published throughout December 2021. The summary relative risk (RR) and 95% confidence interval (CI) were estimated using the DerSimonian-Laird random-effects model. The summary RR for the highest vs. lowest milk intake was 0.83 (95% CI = 0.69–1.00; p = 0.05; I2 = 60%; seven studies) for breast cancer, 0.98 (95% CI = 0.72–1.32; p = 0.88; I2 = 51%; four studies) for prostate cancer, and 0.90 (95% CI = 0.42–1.93; p = 0.78; I2 = 83%; three studies) for colorectal cancer. No evidence of an association emerged in subgroup analyses of menopausal status, cancer stage, fat content of milk, life stage of milk intake, or study design. Consistent results were observed in the meta-analyses using total dairy intake. In conclusion, milk intake during childhood and adolescence might not be associated with risks of breast, prostate, and colorectal cancer later in life. Given the small number of studies that were included in our meta-analysis, and the high heterogeneity, more studies are warranted for a definitive conclusion.
Strong Declines of the White Stork Ciconia ciconia Population in South-Western Poland: A Differentiated Importance of Altitude and Land Use Changes

Abstract. Studies of long-term trends in avian populations require large-scale data not available for most bird species. The White Stork Ciconia ciconia is unique being monitored for over a century and well-reflecting modern environmental changes. Its worldwide and national populations are estimated every 10 years thanks to the International White Stork Censuses (IWSC). We present the yet unpublished data of the last IWSC 2014 from the main species stronghold, Poland. We assessed the population size and compared its changes with the previous IWSC 2004 in 23% of the country area, including mountains. We looked for potential drivers of trends among land use transformations, checked population changes along altitudinal gradient, and compared the long-term trends in the global, national and regional populations. In 2014, 2560 pairs (3.61 pairs/100 km2) bred in south-western Poland, a decline by 35.5% compared to 2004. Decreases were strongly inversely related to the altitude, i.e. declines in lowlands were twice as high as in the mountains. Changes in area of grasslands, croplands, forests, and built-up areas were all weak predictors of the decline. Stork decrease in south-western Poland contradicts a stable country-wide trend in 2004–2014 (although the latest countrywide data also suggest a decline), which is inconsistent with the increase of the global population. Heterogeneity in trends indicates that Poland could be currently viewed as a transitional area, lying between large breeding areas inhabited by increasing stork populations. Notably, the western border between areas of increases and decreases coincides with the division into two migratory populations suggesting that the heterogeneity of trends may be related to migration paths and wintering grounds. Overall, our data confirm recent range shift of the species, and show difficulties in drawing general conclusions on stork demography based on regional data.
Remote Sensing Image Classification Using CNN-LSTM Model

The image classification of remote sensing (RS) plays a significant role in earth observation technology using RS data, extensively used in the military and civic sectors. However, the RS image classification confronts substantial scientific and practical difficulties because of RS data features, such as high dimensionality and relatively limited quantities of labeled examples accessible. In recent years, as new methods of deep learning (DL) have emerged, RS image classification approaches using DL have made significant advances, providing new possibilities for RS image classification research and development. Most of the researchers are using CNN to classify remote sensing images, but CNN alone problem with sequence data processing. But to get some sense out of the classification of remote sensing images. To avoid this in this paper, we use the CNN-LSTM model. The model performed ineffective classification of remote sensing images; the experimental results show that the proposed model is effective in classifying remote sensing images.