Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-compassion as a Newly Observed Dimension of the Student's Personality

The mindfulness-based methods are on the rise in the mental health care of students as well as employees. Therefore, the research on self-compassion is necessary to explore abilities and personality traits that are cultivated by the mindfulness approach. Our research deals with the assessment of the level of self-compassion by the students of the Faculty of Economics and Management at the Czech University of Life Sciences to precise the planned mindfulness-based intervention. Further, the gender and personality specifics as well as a connection to academic achievement are examined. For this purpose, the Self-compassion Scale, and the NEO-PI-R were used. The results proved insignificant correlations between the self-compassion subscales and self-reported grades, but also subtle differences in the structure of the self-compassion by males and females. Further, correlations between the neuroticism and the Self-compassion Scale and its subscales were revealed. Structural equation modeling was involved to gain more complex insight in the researched area.
Development of Science e-Book Containing Gamelan's Local Wisdom Based on STEAM-POE to Facilitate Students' Love of Local Culture

Science learning is still not integrated with the surrounding environment so that students tend not to be able to recognize the local wisdom of their area. Even though science learning contains local wisdom, it can increase students' love of local culture, creating contextual and meaningful learning. To overcome this, research was carried out through the development of a science e-book containing gamelan's local wisdom based on STEAM-POE. This development research aims to (1) determine the validity of a science e-book containing gamelan's local wisdom based on STEAM-POE (2) find out student’s love local culture. This study uses the Thiagarajan 4D development model (Four-D Model), which consists of four stages, namely the definition stage, the design stage, and the development stage. The dissemination stage was not carried out, this research was only limited to product feasibility testing. The research design used was the one-group posttest-only design. The sample in this study was class VIII E, totaling 32 people. The collection techniques used were questionnaires and documentation. The results of the material expert validity test showed that the science e-book containing gamelan's local wisdom based on STEAM-POE was declared very valid with an Aiken's V value of 0.992 and the validity of the teaching material expert showed that the science e-book containing gamelan's local wisdom based on STEAM-POE was declared very valid with Aiken's V score of 1.0. Students' love of local culture after using the science e-book containing local gamelan's local wisdom based on STEAM-POE shows a score of 3.266 in the very good category. Students' love of local culture also gets a variable score of 52.5 which is included in the high category.
Unsteady MHD Flow Past Impulsively Started Vertical Plate in Porous Medium with Heat Source and Chemical Reaction

An unsteady MHD free convective flow of an incompressible, electrically conducting, viscous fluid along an infinitely long vertical plate with an impulsive motion in the presence of heat source and homogeneous binary chemical reaction in a rotating system is studied here. The dimensionless form of governing equations is obtained and solved analytically with the help of Laplace transform method. The solutions obtained for concentration, temperature and velocity distribution are analyzed graphically. The numerical values of the skin friction at the plate are shown in the table. It is noticed that for a strong magnetic field the flow pattern is affected significantly by Hall current at a different rotation. The results obtained may be useful for applications in the area related to the structure of rotating magnetic stars, the solar physics which deals the sunspot development, and the solar cycle, etc.
THE EVALUATION OF STRUCTURE-FROM-MOTION WORKFLOW WITH THE TLS SYNTHETIC IMAGES SIMULATOR – THE CULTURAL HERITAGE APPROACH

Abstract. Modern measurement technologies such as Terrestrial Laser Scanning or combined Structure-from-Motion with Multi-View Stereo are commonly utilised to monitor, preserve and document cultural heritage objects and sites. For this reason, it is essential to know the capabilities and limitations of the sensor used, the data processing methods, and in particular, the orientation of the images. However, these algorithms tackle different errors and have different effects on the final accuracy of images orientation. For this reason, it is essential to know how the algorithms implemented in the Structure-from-Motion approach work. Due to the impossibility of obtaining this information for commercial solutions, it is necessary to use synthetic data to assess the quality of the SfM process. Therefore, this article aims to present the method of evaluation of SfM approach implemented in commercial Agisoft Metashape and COLLMAP open-source software based on the synthetic data generated from TLS point clouds of three different Cultural Heritage sites. In addition, obtained results were compared with the author's SfM approach based on BRISK, FAST, CenSurE, SIFT and SURF (and its Affine detectors equivalents) detector implemented (Fig. 1) and Learned-based -feature extraction approach SuperGlue and LoFTR. The second aim of this research is to propose an application to automatically generate scalable benchmark based on point clouds or 3D models of cultural heritage objects.
Detection of Coronaviruses in Bats in Lebanon during 2020

Bats are considered the main reservoir of coronaviruses (CoVs), and research evidence suggests the essential role of bats in the emergence of Severe Acute Respiratory Syndrome Coronaviruses (SARS-CoV) and SARS-CoV-2. SARS-CoV-like viruses have been recently detected in bats in different countries. In 2020, we conducted surveillance for CoVs among six different bat species in Lebanon. Of 622 swab specimens taken, 77 tested positive. Alpha- and Beta- CoVs were identified in samples collected from different species. Our results show that SARS-like coronaviruses circulate in bats in this region, and we provide new data on their genetic diversity. The interaction between the spike of the detected SARS-CoV-like viruses and the human angiotensin-converting enzyme 2 (hACE2) receptor could be crucial in understanding the origin of the epidemic. The 3D protein structure analysis revealed that the receptor-binding domains of the SARS-like virus identified in Lebanon bind to the hACE2 protein more efficiently than to the spike of the SARS-CoV-2 strain. The spike of the detected SARS-CoV-like viruses does not contain the recognition site of furin at the cleavage site. Thus, our study highlights the variety of bat coronaviruses in Lebanon and suggests the zoonotic potential for other SARS-CoV-like viruses.
Spatial econometric Monte Carlo studies: raising the bar

We discuss Monte Carlo methodology that can be used to explore alternative approaches to estimating spatial regression models. Our focus is on models that include spatial lags of the dependent variable, e.g., the SAR specification. A major point is that practitioners rely on scalar summary measures of direct and indirect effects estimates to interpret the impact of changes in explanatory variables on the dependent variable of interest. We argue that these should be the focus of Monte Carlo experiments. Since effects estimates reflect a nonlinear function of both $$\beta $$β and $$\rho $$ρ, past studies’ focus exclusively on $$\beta $$β and $$\rho $$ρ parameter estimates may not provide useful information regarding statistical properties of effects estimates produced by alternative estimators. Since effects estimates have recently become the focus of inference regarding the significance of (scalar summary) direct and indirect impacts arising from changes in the explanatory variables, empirical measures of dispersion produced by simulating draws from the (estimated) variance–covariance matrix of the parameters $$\beta $$β and $$\rho $$ρ should be part of the Monte Carlo study. An implication is that differences in the quality of estimated variance–covariance matrices arising from alternative estimators also plays a role in determining the accuracy of inference. An applied illustration is used to demonstrate how these issues can impact conclusions regarding the performance of alternative estimators.
Heart Rate Recovery as a Predictor of Long-Term Adverse Events after Negative Exercise Testing in Patients with Chest Pain and Pre-Test Probability of Coronary Artery Disease from 15% to 65%

Background: The prognosis of patients with chest pain after a negative exercise test is good, but some adverse events occur in this low-risk group. The aim of our study was to identify predictors of long-term adverse events after a negative exercise test in patients with chest pain and a lower intermediate (15–65%) pre-test probability of coronary artery disease (CAD) and to assess the prognostic value of exercise electrocardiography and exercise stress echocardiography in this group of patients. Methods: We identified from our stress test laboratory database 862 patients with chest pain without previously known CAD and with a pre-test probability of CAD ranging from 15 to 65% (mean 41 ± 14%) who underwent exercise testing. Patients were followed for the occurrence of death, non-fatal myocardial infarction (MI) and clinically guided revascularization. Results: During the median follow-up of 94 months, 87 patients (10.1%) had an adverse event (AE). A total of 30 patients died (3.5%), 23 patients suffered non-fatal MI (2.7%) and 34 patients (3.9%) had clinically guided revascularization (20 patients percutaneous and 14 patients surgical revascularizations). Male gender, age, the presence of diabetes and a slow heart rate recovery (HRR) in the first minute after exercise were independently related to the occurrence of AEs. Adverse events occurred in 10.3% of patients who were tested by exercise stress echocardiography and in 10.0% of those who underwent stress electrocardiography (p = 0.888). Conclusion: The risk of AEs after negative exercise testing in patients with a pre-test probability of CAD of 15–65% is low. Male patients with a history of diabetes and slow HRR in the first minute after exercise have an increased risk of an adverse outcome.
Bayesian Variable Selection Regression of Multivariate Responses for Group Data

We propose two multivariate extensions of the Bayesian group lasso for variable selection and estimation for data with high dimensional predictors and multi-dimensional response variables. The methods utilize spike and slab priors to yield solutions which are sparse at either a group level or both a group and individual feature level. The incorporation of group structure in a predictor matrix is a key factor in obtaining better estimators and identifying associations between multiple responses and predictors. The approach is suited to many biological studies where the response is multivariate and each predictor is embedded in some biological grouping structure such as gene pathways. Our Bayesian models are connected with penalized regression, and we prove both oracle and asymptotic distribution properties under an orthogonal design. We derive efficient Gibbs sampling algorithms for our models and provide the implementation in a comprehensive R package called MBSGS available on the Comprehensive R Archive Network (CRAN). The performance of the proposed approaches is compared to state-of-the-art variable selection strategies on simulated data sets. The proposed methodology is illustrated on a genetic dataset in order to identify markers grouping across chromosomes that explain the joint variability of gene expression in multiple tissues.
Tensile Strength Improvements of Ramie Fiber Threads through Combination of Citric Acid and Sodium Hypophosphite Cross-Linking

Ramie (Boehmeria nivea) is believed to be one of the strongest natural fibers, but it still remains behind synthetic materials in terms of tensile strength. In this study, ramie materials were prepared to evaluate the modification crosslinking effect of natural fiber. The aim is to optimize various concentrations of citric acid (CA) crosslinking by adding Sodium hypophosphite (NaPO2H2), which is activated at different temperatures, to obtain the highest tensile mechanical strength. This crosslinking effect has been confirmed by FTIR to show the esterification process in the molecular structure of cellulose. The changes in the character of the fiber surface were analyzed by SEM. The tensile strength increased from 62.33 MPa for 0% CA to 124–172.86 MPa for decorticated fiber with a CA concentration of 0.75–1.875% (w/w). A significant increase in tensile strength was observed more than 19 times when CA/SHP 1% was treated at an activation temperature of 110 °C with a superior tensile strength of 1290.63. The fiber crosslinked with CA/SHP should be recommended for application of Natural Fiber Reinforced Polymer Composite (NFRPC), which has the potential to use in functional textile and industrial sector automotive or construction.
Aerodynamic lift coefficient prediction of supercritical airfoils at transonic flow regime using convolutional neural networks (CNNs) and multi-layer perceptions (MLPs)

Designing an aircraft involves a lot of stages, however, airfoil selection remains one of the most crucial aspects of the design process. The type of airfoil chosen determines the lift on the aircraft wing and the drag on the aircraft fuselage. When a potential airfoil is identified, one of the first steps in deciding its optimality for the aircraft design requirements is to obtain its aerodynamic lift and drag coefficients. In the early stages of trying to select a candidate airfoil, which a whole part of the design process rests on, the conventional method for acquiring the aerodynamic coefficients is through Computational Fluid Dynamics Simulations (CFDs). However, CFD simulation is usually a computationally expensive, memory-demanding, and time-consuming iterative process; to circumvent this challenge, a data-driven model is proposed for the prediction of the lift coefficient of an airfoil in a transonic flow regime. Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) were used to develop a suitable model which can learn a set of usable patterns from an aerodynamic data corpus for the prediction of the lift coefficients of airfoils. Findings from the training revealed that the models (MLPs and CNNs) were able to accurately predict the lift coefficients of the airfoil.
Dusty Radiative Transfer Modeling of the Ultraviolet to Infrared Spectral Energy Distributions of Nearby Galaxies

In normal star forming galaxies without active galactic nuclei, stars and dust dominate the energy output in the ultraviolet (UV), optical and infrared (IR). Knowledge of stellar populations and dust is crucial to understanding the evolution of galaxies in the universe. The problems of dust and stars are tightly related because dust absorbs stellar radiation in the UV and optical, and re-radiates it in the IR. To study the properties of interstellar dust and stellar populations, we fit the global spectral energy distributions (SEDs) of nearby galaxies using radiative transfer models. Thanks to various space missions in the last few decades, the data required to construct the full UV to IR SEDs of nearby galaxies is available. To date, systematic studies on interstellar dust and stellar populations rely on simplified connections between the UV and IR such as energy conservation. Based on dust grain physics, radiative transfer models physically couple the UV and IR for consistent radiation fields and dust emission spectra, but is computationally challenging. With the use of supercomputer facilities, we compute a large grid of radiative transfer models spanning the physical range of dust and stellar parameters. Fitting SEDs with the grid is one step towards ii
Response of Maize Seedlings to Silicon Dioxide Nanoparticles (SiO2NPs) under Drought Stress

Recently, the use of nanofertilizers has received a great deal of attention in managing plants under biotic and abiotic stresses. However, studies that elucidate the role of silicon dioxide nanoparticles (SiO2NPs) in regulating maize tolerance to drought stress are still at early stages of development. In this study, plants that were treated with SiO2NPs (0.25 g/L as foliar spray) displayed considerable improvement in the growth indices, despite being subjected to drought stress. In addition, the action of SiO2NPs led to a considerable rise in the levels of chlorophylls, proline, cell membrane integrity, leaf water content, and antioxidant enzymes (superoxide dismutase (SOD), catalase (CAT), and guaiacol peroxidase (G-POX)). In contrast, an inverse trend was seen in the oxidative injury, the total amount of soluble sugars, and the activity of ascorbate peroxidase (APX). At the same time, carotenoids were unaffected in SiO2NPs-treated and non-treated plants under drought stress. The results of the molecular investigation that was conducted using qRT-PCR showed that the relative expression of the D2 protein of photosystem II (PsbD) was elevated in SiO2NPs-treated plants in response to drought stress, while the expression of the osmotic-like protein (OSM-34) and aquaporin (AQPs) was downregulated in SiO2NPs-treated plants in response to drought stress. This research could pave the way for further investigations into how SiO2NPs boost plant resistance to drought stress.
Highly Effective Extraction of Gigantol from Dendrobium officinale Using the Ultrasonic-assisted Dry Grinding

Background Dendrobium officinale is a valuable medicinal food plant in China, and its component gigantol has various biological activities. However, due to the low extraction rate of gigantol, its application in the functional food industry is limited. This research aimed to explore an effective gigantol extraction method. Results Ultrasonic-assisted dry grinding extraction of gigantol from D. officinale almost reached completion after 15 min. The extraction rate of gigantol reached 65%, which was higher than that of the conventional heating refluxing method of extraction. Most cells were broken, and particle size was approximately 410 nm when D. officinale was ground for 15 min. Conclusion The results indicated that ultrasonic-assisted dry grinding is a highly effective gigantol extraction method that is valuable for the efficient use and low-cost extraction of gigantol from the medicinal food plant D. officinale. Furthermore, this method will be beneficial for the future application of gigantol in the functional food industry.
Semi-global inversion of vp to vs ratio for elastic wavefield inversion

We introduce an approach to estimate the ratio between P- and S-wave velocities, vp/vs, in the scope of elastic full waveform inversion (FWI). Elastic FWI is generally implemented with local optimization methods relying on initial estimates of the long wavelengths of P- and S-wave models. However, successful inversions can be hindered if an accurate enough relation between vp and vs velocities is not used as a constraint. This relation can be estimated from empirical relations. Herein, we introduce an alternative approach based upon a semi-global inversion scheme. We observe that for a large number of cases, and particularly in the context of FWI, vp/vs can be represented on a sparse basis. This sparse basis has a much smaller dimension than that of the typical model space in elastic FWI. This creates the possibility of using global optimization methods. The optimal estimate of vp/vs is obtained with quantum particle swarm optimization (QPSO). This method probes a population of possible models. The assessment of each model of vp/vs in the population is obtained with nested local iterations updating for vp only. Conventional elastic FWI is then carried out for jointly estimating high-resolution models of vp and vs. We demonstrate with synthetic examples that the estimates of vp are relatively robust to errors in the estimated vp/vs, and that effectively a sparse representation of the model of vp/vs is feasible for the reconstruction of a model of vs. We also demonstrate that the proposed approach performs better than constraining elastic FWI with an empirical relation between vp and vs, leading to improved estimates of models of vp and vs from seismic data.
Modal frequencies evaluation of a damaged bridge using RCVMD algorithm based on sensor dynamic responses

This paper aims to employ high-rate global navigation satellite system real-time kinematic (GNSS-RTK) and triaxial accelerometer sensors to evaluate the modal frequencies of a damaged long-span bridge based on the proposed modified variational mode decomposition algorithm. Firstly, in the modified VMD, the relative root mean square error was presented to optimize the decomposition parameter K, the correlation criterion was introduced to select the suitable intrinsic mode function components. The above procedure is called the RCVMD algorithm. Subsequently, the performance comparison evaluation experiments proved that the RCVMD algorithm was more advantageous in the decomposition and reconstruction of signals, had a better noise reduction effect in the signal with different noise levels, and was suitable to process the actual measurement response. Finally, in the field experiment, the modal frequencies of the damaged bridge were correctly detected from the GNSS-RTK displacement and acceleration responses according to the RCVMD algorithm, and the accelerometer picked up the high-order structural frequencies, demonstrating the reliability of the proposed method. The frequency characteristics of the damaged bridge are different from the normal state. The first and third-order frequencies increase, which is especially obvious in the first-order frequency, yet the second-order frequency decreases. The measured results have practical applications and can provide data support for the maintenance of the damaged bridge.
Is Basketball a Symmetrical Sport?

Basketball, an intermittent sport with a high impact load, presents a strong probability of lower limb injuries. These injuries can be caused by poor quantification of loads, very intense training sessions or even asymmetries in the lower extremities. The main aim of the present study is to identify whether asymmetries exist in basketball. Specifically, asymmetries depending on: (i) type of task, (ii) type of game situation, (iii) specific positions while training and (iv) specific positions while competing. It is hypothesized that there will be no significant differences between the different conditions. For this purpose, all the training sessions and matches of a professional basketball team belonging to the First Spanish Division were monitored during the preseason. WIMU PROTM inertial devices were used for data collection. The statistical analysis compared the different cases with an ANOVA test. The results do not show significant differences in the values collected among the type of task, the game situation and the positions of the individual players. It can be concluded that basketball is a symmetrical sport. Despite this, the coaching staff should carefully monitor the training loads and asymmetries of the players to avoid the risk of injury.