Miracle-dz/UniFAR-FTUs huggingface dataset:
Zoom supported emergency remote teaching and learning in teacher education: A case study from Hong Kong

The shift from face-to-face teaching to emergency remote teaching has become the new normal due to the pandemic in 2020. However, little research has been conducted in Hong Kong to investigate pre-service teachers’ perceptions of the immediate adoption and delivery of online face-to-face teaching. In response to this call, the present research conducted a questionnaire survey with 48 pre-service teachers at a Hong Kong university to examine their perceptions of Zoom as a viable alternative to the suspended traditional face-to-face delivery mode of instruction. It also discussed key factors that lead to better online learning outcomes. The results showed that the participants had overall positive perceptions of Zoom-supported synchronous online education. The Zoom feature that most students considered useful was screen sharing on computers. Zoom-supported synchronous online learning benefited students by helping them to attend class meetings remotely, study course materials and content, communicate and collaborate with their instructors and classmates, and develop a sense of community and social presence. Four main factors may influence learners’ perceptions of synchronous online learning: the quality of online communication and collaboration, learning efficiency, learner autonomy and the usability of technology.
Discrimination against Working Women and Its Impact on Their Work Performance in District Sahiwal

Workplace gender discrimination comes in many forms: unequal pay, the disparity in promotions, incidents of sexual harassment, and fewer opportunities, which means that an employee is treated differently or less favorably because of their gender identity. Around the world, four-in-ten working women face discrimination on the job because of their gender. This research explored the issue of discrimination against working women at the workplace and its impact on their work performance in District Sahiwal, Punjab, Pakistan. The quantitative data were collected through a cross-sectional survey. A multistage random sampling technique was used for the data collection. A self-designed interview schedule was administered to 200 females of lower, middle, and higher category employees of public and private institutions of District Sahiwal. In the data analysis, both descriptive and inferential statistical techniques were used. Research findings revealed that females were discriminated more than males in the private sector than in the public sector. The results indicated that 63% of women have adverse physical and mental effects due to discrimination at the workplace. In this regard, 60% of women believed that workplace discrimination increased stress and frustration and reduced job satisfaction, commitment, and motivation. The bivariate analysis also shows that gender discrimination decreases satisfaction and commitment and increases stress levels among working women. Therefore, this study suggested that the government should immediately strengthen legal protections against workplace discrimination based on gender.
Improved area-based deformation analysis of a radio telescope’s main reflector based on terrestrial laser scanning

Abstract The main reflectors of radio telescopes deform due to gravitation when changing their elevation angle. This can be analyzed by scanning the paraboloid surface with a terrestrial laser scanner and by determining focal length variations and local deformations from best-fit approximations. For the Effelsberg radio telescope, both groups of deformations are estimated from seven points clouds measured at different elevation angles of the telescope: the focal length decreases by 22.7 mm when tilting the telescope from 90 deg to 7.5 deg elevation angle. Variable deformations of ± 2 mm are detected as well at certain areas. Furthermore, a few surface panels seem to be misaligned. Apart from these results, the present study highlights the need for an appropriate measurement concept and for preprocessing stepswhen using laser scanners for area-based deformation analyses. Especially, data reduction, object segmentation and laser scanner calibration are discussed in more detail. An omission of these steps would significantly degrade the deformation analysis and the significance of its results. This holds for all sorts of laser scanner based analyses.
Strong Effect of Stress on the Seismic Signature of the Post‐Stishovite Phase Transition in the Earth's Lower Mantle

The stishovite to post‐stishovite phase transition may modify the scattering of seismic waves by stishovite‐bearing rocks in the Earth's lower mantle. A series of continuous compression experiments on sintered polycrystalline stishovite was performed to study the effect of stress on the phase transition. The experimental results show that the phase transition shifts to lower pressures as the magnitude of deviatoric stress increases. Our results further show that the bulk modulus of sintered polycrystalline stishovite differs from that derived from single crystal measurements and decreases at the phase transition. In cold regions, such as subducted slabs, stresses may accumulate and shift the phase transition to a shallower depth. In hot regions with less stress, such as rising plumes, the phase transition is shifted to a greater depth. In addition, the phase transition may have varying seismic signatures depending on the behavior of the grain boundaries in mantle rocks and the micro‐stresses present in neighboring grains.
Diagnostic Workup and Outcome in Patients with Profound Hyponatremia

Hyponatremia is the most common electrolyte disorder. A proper diagnosis is important for its successful management, especially in profound hyponatremia. The European hyponatremia guidelines point at sodium and osmolality measurement in plasma and urine, and the clinical evaluation of volume status as the minimum diagnostic workup for the diagnosis of hyponatremia. We aimed to determine compliance with guidelines and to investigate possible associations with patient outcomes. In this retrospective study, we analysed the management of 263 patients hospitalised with profound hyponatremia at a Swiss teaching hospital between October 2019 and March 2021. We compared patients with a complete minimum diagnostic workup (D-Group) to patients without (N-Group). A minimum diagnostic workup was performed in 65.5% of patients and 13.7% did not receive any treatment for hyponatremia or an underlying cause. The twelve-month survival did not show statistically significant differences between the groups (HR 1.1, 95%-CI: 0.58–2.12, p-value 0.680). The chance of receiving treatment for hyponatremia was higher in the D-group vs. N-Group (91.9% vs. 75.8%, p-value < 0.001). A multivariate analysis showed significantly better survival for treated patients compared to not treated (HR 0.37, 95%-CI: 0.17–0.78, p-value 0.009). More efforts should be made to ensure treatment of profound hyponatremia in hospitalised patients.
Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network

Deep learning and digital image technologies have combined to create a potentially effective tool for identifying partial discharge (PD) patterns precisely. However, it is necessary to investigate which algorithm guarantees the best performance. The more common tools are restricted by a lack of training data and an advanced model in itself. Therefore, the main goal of this paper is to develop an efficient hybrid network comprising two deep networks, long short-term memory (LSTM), and convolutional neural network (CNN), for identifying the form of PD. A total of <inline-formula> <tex-math notation="LaTeX">$8186\times 25$ </tex-math></inline-formula> (non-PD<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>PD) images were applied to assess the proposed methods. The size of the PD type was increased to 3675 images using data augmentation techniques. The results indicated that the integration of CNN and LSTM networks can provide a more robust implementation for PD detection. The integrated CNN-LSTM deep network based on data augmentation outperformed features derived from a single deep network. The recall, F-measure, and classification precision have 99.9% as a validation accuracy with a 99.8% intersection over union and a loss of 0.004.
Confocal position alignment in high-precision wavefront error metrology using Shack-Hartmann wavefront sensor

The aberration inspection of Shack-Hartmann of lithographic lens has reached the nanometer inspection accuracy. Collimator as the key element of the system, the accurate positioning of itself is one important factor for the inspection accuracy. Based on the wavefront reconstruction with Zernike polynomials, in this paper, an optical alignment method for positioning adjustments of the collimator is presented. A sensitivity matrix is obtained from the equation that describes the correlation between Zernike coefficients and the multi-degree-of-freedom misalignment, and the positioning adjustments of collimator are acquired thereof. For the aberration inspection with Shack-Hartmann method, an engineering model of 193nm NA 0.75 projection lens is established in commercial simulating software (ZEMAX). For 0.5nm RMS aberration inspection accuracy, the positioning accuracy of collimator is analyzed and plotted with independent single freedom degree and mutual correlation with combined three freedom degrees. These analysis indicate the proposed method is a viable tool for aligning confocal position of collimator.
Some non-standard statistical dependence problems

The major result of this thesis is the development of a framework for the application of pair-mixtures of copulas to model asymmetric dependencies in bivariate data. The main motivation is the inadequacy of mixtures of bivariate Gaussian models which are commonly fitted to data. Mixtures of rotated single parameter Archimedean and Gaussian copulas are fitted to real data sets. The method of maximum likelihood is used for parameter estimation. Goodness-of-fit tests performed on the models giving the highest log-likelihood values show that the models fit the data well. We use mixtures of univariate Gaussian models and mixtures of regression models to investigate the existence of bimodality in the distribution of the widths of autocorrelation functions in a sample of 119 gamma-ray bursts. Contrary to previous findings, our results do not reveal any evidence of bimodality. We extend a study by Genest et al. (2012) of the power and significance levels of tests of copula symmetry, to two copula models which have not been considered previously. Our results confirm that for small sample sizes, these tests fail to maintain their 5% significance level and that the Cramér−von Mises-type statistics are the most powerful.
Bottom‐Up Evaluation of the Methane Budget in Asia and Its Subregions

A comprehensive methane (CH4) budget, including spatial and temporal patterns of sources and sinks, is pivotal for effective climatic mitigation and prediction. This study used a bottom‐up approach to evaluate the CH4 budget of Asia during 1970–2021. Natural sources and sinks were evaluated using a process‐based biogeochemical model and with ground and satellite observations. Anthropogenic sources were evaluated using emission inventories produced by socioeconomic studies. During the 1970s and 2010s, anthropogenic emissions accounted for 78% and 84% of the emitted total CH4 budget of 140.2 and 207.6 Tg CH4 yr−1, respectively. Anthropogenic sources were responsible for a 49% increase in total emissions. During 2001–2021, Central, East, South, Southeast, and West Asia accounted for 3.3%, 35.1%, 25.6%, 23.1%, and 13.0%, respectively, of Asian emissions. Sectoral compositions and temporal variations differed among subregions. Emissions from paddy field soils in Asia were smaller in 2021 (33.6 Tg CH4 yr−1) than in 1970 (39.2 Tg CH4 yr−1) because of changes in agricultural production. Among natural sources, wetlands accounted for 63% of total emissions in East Asia, but geological emissions predominated in Central and West Asia. Interannual variability of the net CH4 budget was mainly due to wetland and biomass burning emissions, whereas decadal variability was due to anthropogenic sectors. δ13C‐CH4 and isoflux analysis of sector‐specific fluxes and isotopic ratios indicated the effects of the regional budget on atmospheric trends. Bottom‐up evaluations of regional budgets can provide information critical to the Global Stocktake of the Paris Agreement, the Global Methane Pledge, and other related actions.
3D Modeling for Multimodal Visualization of Medical Data

Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to produce anatomical images and physiological information of the brain. Based on the principle of magnetic resonance, this non-invasive tool provides numerous types of advanced acquisitions. Some of these acquisitions can provide information on the concentrations of molecules within a well-localized volume in the cortex. Several studies have shown the important role of modeling and visualizing these concentrations according to brain anatomy and in high resolution to improve the diagnosis and treatment of tumor areas. To visualize this data, physicians use the concentrations estimated after an acquisition in a grid composed of large voxels. In this acquisition grid, each voxel has a concentration value distributed evenly over its entire 3D space. Interpolation algorithms are usually applied on this grid in order to improve the spatial resolution. However, none of them takes into account the anatomical composition of the acquisition volume, which is a mixture of three substances that constitute a brain for a healthy subject, while in the case of a tumor subject, it also includes the presence of a tumor. These partial volumes, which make up the acquisition volume, admit very di(cid:27)erent molecular concentrations.
Variable-temperature solid-state NMR analysis of woody materials in the presence of small hydroxyl molecules

Abstract Interactions between the hierarchical structure of Japanese cypress and small hydroxylic solvents with high polarity were studied using variable-temperature 13C cross-polarization and magic-angle spinning nuclear magnetic resonance (CP/MAS) NMR spectroscopy, including low-temperature measurements. 13C CP/MAS NMR spectra were enhanced by dipolar interactions closely related to 1H–13C magnetization transfer, providing information about the interaction between woody materials and small hydroxyl molecules. The change in interaction was particularly near the melting point of the hydroxylic solvent, where a decrease in the intensity of the cypress carbohydrate signal and the appearance of the signal of solvent molecules were observed. The trend of signal intensity in the variable-temperature 13C CP/MAS NMR spectra of cypress and cellulose fibers in the presence of hydroxylic solvents indicated that the decrease in the carbohydrate signal intensity due to the transfer of magnetization near the melting point of the hydroxyl molecule is greater when the size of the hydroxyl molecule is smaller. Furthermore, molecular association in the nanopores of the hierarchical structure allowed the hydroxyl molecular signals of the impregnated cypress to appear above the melting point.
Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection‐Permitting Simulations for Data‐Driven Parameterizations

Atmospheric gravity waves (GWs) span a broad range of length scales. As a result, the un‐resolved and under‐resolved GWs have to be represented using a sub‐grid scale (SGS) parameterization in general circulation models (GCMs). In recent years, machine learning (ML) techniques have emerged as novel methods for SGS modeling of climate processes. In the widely used approach of supervised (offline) learning, the true representation of the SGS terms have to be properly extracted from high‐fidelity data (e.g., GW‐resolving simulations). However, this is a non‐trivial task, and the quality of the ML‐based parameterization significantly hinges on the quality of these SGS terms. Here, we compare three methods to extract 3D GW fluxes and the resulting drag (Gravity Wave Drag [GWD]) from high‐resolution simulations: Helmholtz decomposition, and spatial filtering to compute the Reynolds stress and the full SGS stress. In addition to previous studies that focused only on vertical fluxes by GWs, we also quantify the SGS GWD due to lateral momentum fluxes. We build and utilize a library of tropical high‐resolution (Δx = 3 km) simulations using weather research and forecasting model. Results show that the SGS lateral momentum fluxes could have a significant contribution to the total GWD. Moreover, when estimating GWD due to lateral effects, interactions between the SGS and the resolved large‐scale flow need to be considered. The sensitivity of the results to different filter type and length scale (dependent on GCM resolution) is also explored to inform the scale‐awareness in the development of data‐driven parameterizations.
Pseudosymmetric and Weyl-pseudosymmetric $(\kappa , \mu )$-contact metric manifolds

In this paper we classify pseudosymmetric and Ricci-pseudosymmetric $(\kappa , \mu )$-contact metric manifolds in the sense of Deszcz. Next we characterize Weyl-pseudosymmetric $(\kappa , \mu )$-contact metric manifolds.
Thermodynamic Modeling of the Amorphous Solid Dispersion-Water Interfacial Layer and Its Impact on the Release Mechanism

During the dissolution of amorphous solid dispersion (ASD) formulations, the gel layer that forms at the ASD/water interface strongly dictates the release of the active pharmaceutical ingredient (API) and, hence, the dissolution performance. Several studies have demonstrated that the switch of the gel layer from eroding to non-eroding behavior is API-specific and drug-load (DL)-dependent. This study systematically classifies the ASD release mechanisms and relates them to the phenomenon of the loss of release (LoR). The latter is thermodynamically explained and predicted via a modeled ternary phase diagram of API, polymer, and water, and is then used to describe the ASD/water interfacial layers (below and above the glass transition). To this end, the ternary phase behavior of the APIs, naproxen, and venetoclax with the polymer poly(vinylpyrrolidone-co-vinyl acetate) (PVPVA64) and water was modeled using the perturbed-chain statistical associating fluid theory (PC-SAFT). The glass transition was modeled using the Gordon–Taylor equation. The DL-dependent LoR was found to be caused by API crystallization or liquid-liquid phase separation (LLPS) at the ASD/water interface. If crystallization occurs, it was found that API and polymer release was impeded above a threshold DL at which the APIs crystallized directly at the ASD interface. If LLPS occurs, an API-rich phase and a polymer-rich phase are formed. Above a threshold DL, the less mobile and hydrophobic API-rich phase accumulates at the interface which prevents API release. LLPS is further influenced by the composition and glass transition temperature of the evolving phases and was investigated at 37 °C and 50 °C regarding impact of temperature of. The modeling results and LoR predictions were experimentally validated by means of dissolution experiments, microscopy, Raman spectroscopy, and size exclusion chromatography. The experimental results were found to be in very good agreement with the predicted release mechanisms deduced from the phase diagrams. Thus, this thermodynamic modeling approach represents a powerful mechanistic tool that can be applied to classify and quantitatively predict the DL-dependent LoR release mechanism of PVPVA64-based ASDs in water.
Genotype X environemnt interactions and adaptability with sustaiabilty index their comparision in aromatic rice (Oryza sativa L.) under transplanted condition

Seven release varieties of aromatic rice (Oryza Sativa L.) were evaluated for stability of grain yield in complete randomized block design during kharif 2006–2010, at Agricultural Research station, Ummedganj Farm, Kota (Raj) under transplanted condition of South-Eastern plane Zone of Rajasthan. The genotypes yield, regression co efficient (bi), deviation from regression (S2d) with sustainability index was used to identify the stability and adaptability of genotypes. Pooled analysis of variance showed highly significant differences among environments (year), genotypes and GXE interaction. Sufficient mean squares due to genotype x environment (G x E) interactions indicated that the genotypes interacted considerably with the environmental condition. Both linear and nonlinear components of G x E interactions were significant showing the importance of both components in the expression of the traits. The linear component was significant as against the nonlinear component (Pooled deviation), which revealed that a large portion of G x E interaction was accounted for by the linear regression through pooled deviation was significant. Based on estimated stability parameters and over all mean performance of grain yield, variety P-1121, P-2511 P-1460 and Pusa Basmati-1 were identified as superior,which were well adapted to all the environment, stable with above average yielding ability or highest grain yield (45.75, 45.23,43.38 and 41.08 q/ha−1, respectively) with non-significant bi and s2d values coupled with high sustainability index. This showed that these varieties were better responsive to the favorable environments. Mahi sugandha and Taraori (Checks) was good for low yielding environments (response to poor environment) and contradict with respect to the stability parameters and the sustainability index.
Ethical Reasoning at Work: A Cross-Country Comparison of Gender and Age Differences

This paper uses the IBE Ethics at Work 2018 survey to explore employees’ ethical reasoning and examine gender and age differences across 12 countries. Debates about gender and ethics have been intense since Kohlberg’s theory of moral development, with feminist critiques from Gilligan and others advocating the different voice of women, while the recent arrival of Millennials in the workplace has raised new questions about age/generational differences and ethics. The findings in this study suggest that women and older workers have stronger ethical judgments in the workplace than men and younger workers. These gender and age differences, both among employees and managers, are consistent across countries. This study shows that individual characteristics are important for employees’ ethical reasoning, which affects their ability to make ethical decisions and act ethically. Business ethics research should therefore take greater account of differences between (groups of) employees and their learning needs when examining the effectiveness of ethics policies and instruments, while organizations can improve employee ethical reasoning by adopting diversity-based training programs and ethical leadership.