Miracle-dz/UniFAR-FTUs huggingface dataset:
Power-law setting of steady concentration in a binary solution within a droplet at diffusion-controlled or free-molecular regimes of the droplet binary growth in the vapor-gas environment

The times required for reaching the power-law-in-time growth of the droplet radius after nucleation of a markedly supercritical binary droplet, are found analytically and estimated at diffusion-controlled or free-molecular regimes of isothermal binary vapor condensation. The process of setting steady concentration in the binary solution within the growing droplet at the same regimes of binary condensation has been analyzed. It has been shown that setting the steady droplet concentration has also a power-law character in time. The parameters of the power law are specified for each regime of binary condensation and are linked to thermodynamic and kinetic characteristics of condensing vapors and to the steady concentration established in a binary droplet.
SNPAAMapper-Python: A highly efficient genome-wide SNP variant analysis pipeline for Next-Generation Sequencing data

Currently, there are many publicly available Next Generation Sequencing tools developed for variant annotation and classification. However, as modern sequencing technology produces more and more sequencing data, a more efficient analysis program is desired, especially for variant analysis. In this study, we updated SNPAAMapper, a variant annotation pipeline by converting perl codes to python for generating annotation output with an improved computational efficiency and updated information for broader applicability. The new pipeline written in Python can classify variants by region (Coding Sequence, Untranslated Regions, upstream, downstream, intron), predict amino acid change type (missense, nonsense, etc.), and prioritize mutation effects (e.g., synonymous > non-synonymous) while being faster and more efficient. Our new pipeline works in five steps. First, exon annotation files are generated. Next, the exon annotation files are processed, and gene mapping and feature information files are produced. Afterward, the python scrips classify the variants based on genomic regions and predict the amino acid change category. Lastly, another python script prioritizes and ranks the mutation effects of variants to output the result file. The Python version of SNPAAMapper accomplished the overall speed by running most annotation steps in a substantially shorter time. The Python script can classify variants by region in 53 s compared to 166 s for the Perl script in a test sample run on a Latitude 7480 Desktop computer with 8GB RAM and an Intel Core i5-6300 CPU @ 2.4Ghz. Steps of predicting amino acid change type and prioritizing mutation effects of variants were executed within 1 s for both pipelines. SNPAAMapper-Python was developed and tested on the ClinVar database, a NCBI database of information on genomic variation and its relationship to human health. We believe our developed Python version of SNPAAMapper variant annotation pipeline will benefit the community by elucidating the variant consequence and speed up the discovery of causative genetic variants through whole genome/exome sequencing. Source codes, test data files, instructions, and further explanations are available on the web at https://github.com/BaiLab/SNPAAMapper-Python.
Characterization of paper surfaces by friction profilometry

Friction profilometry is a powerful technique that is suitable for the surface characterization of paper products. In this technique, a stylus-type contact method that resembles papermaking processes is used for evaluating the quality attributes of products. The surface characterization requires both surface roughness and friction measurements. At present, however, few reports have been available regarding characterization of the friction by the surface profilometric method. The objective of this study was to provide guiding principles of a stylus-type contact surface profilometry for determining the friction properties of paper. Another objective was to introduce the concept of the mean absolute deviation (MAD) from the average coefficient of friction as a new friction parameter.
Application of a Head-Up Tilt Table Test in Differentiation Between Epilepsy and Syncope in Children

Background: Distinguishing between seizure and neurally mediated syncope is challenging because of similar consequences and medical history. A head-up tilt test (HUTT) is a non-invasive, simple, and easy test to distinguish between epilepsy and syncope besides detailed history taking. Objectives: This study aimed to differentiate between epileptic events and reflex syncope (any different type of syncope) using the head-upright tilt test. Methods: We studied 59 patients (37 boys and 22 girls) between 4 to 18 years old (mean age, 10.5 ± 3.7 years) with a previous diagnosis of seizure who did not respond well to treatment. All patients underwent HUTT, and the test was positive in 26 patients. There were no significant differences in sex, age, provocative factors, associated syndrome, and family history between negative and positive groups. Results: There was a history of actual syncope in 26.9% of the positive tilt test group compared to 15.15% of the negative test group. Also, there was a positive family history of syncope in the positive tilt test group. Among 26 patients with a positive tilt test, 17 were diagnosed with vasovagal syncope (VVS) vasodepressor type and 9 with mixed type. Antiepileptic drugs were tapered for patients diagnosed with VVS, and they did not show any seizures after 18 ± 6 months of follow-up. Overemphasizing positive family history and inattention to history taking are 2 crucial factors leading to the misdiagnosis of epilepsy. Conclusions: Our study showed that HUTT is a non-invasive test that can be useful, especially for early and proper diagnosis in children with refractory epilepsy.
Towards More Accurate and Complete Heterogeneous Iris Segmentation Using a Hybrid Deep Learning Approach

Accurate iris segmentation is a crucial preprocessing stage for computer-aided ophthalmic disease diagnosis. The quality of iris images taken under different camera sensors varies greatly, and thus accurate segmentation of heterogeneous iris databases is a huge challenge. At present, network architectures based on convolutional neural networks (CNNs) have been widely applied in iris segmentation tasks. However, due to the limited kernel size of convolution layers, iris segmentation networks based on CNNs cannot learn global and long-term semantic information interactions well, and this will bring challenges to accurately segmenting the iris region. Inspired by the success of vision transformer (VIT) and swin transformer (Swin T), a hybrid deep learning approach is proposed to segment heterogeneous iris images. Specifically, we first proposed a bilateral segmentation backbone network that combines the benefits of Swin T with CNNs. Then, a multiscale feature information extraction module (MFIEM) is proposed to extract multiscale spatial information at a more granular level. Finally, a channel attention mechanism module (CAMM) is used in this paper to enhance the discriminability of the iris region. Experimental results on a multisource heterogeneous iris database show that our network has a significant performance advantage compared with some state-of-the-art (SOTA) iris segmentation networks.
A Creative Approach to Knowledge Translation: The Use of Short Animated Film to Share Stories of Refugees and Mental Health

This study used animated film to translate narratives of refugees and mental health into accessible material aimed at enhancing empathy and understanding. It focuses on the use of short animated films in series one and two of the Woven Threads catalogue. Series one shared moments of hope in a refugee’s journey, whilst series two focused on people living with mental health challenges. This research was designed to understand the responses to viewing for people who watch these animations. A mixed-method design was used via an online Qualtrics platform that asked respondents to view two short animated films, one from the refugee series and one from the mental health series. 364 members of the general public viewed and responded to the refugee film and 275 responded to the mental health film. The platform collected both quantitative and qualitative data. Survey responses indicated that the majority of viewers found the films challenged public misconceptions about refugees and individuals with mental health challenges and left them with a feeling of hopefulness. Qualitative narratives were organised into one superordinate theme: the power of film as a knowledge translation strategy, with four subthemes: (i) changing perceptions and inspiring empathy, (ii) enhancing literacy, (iii) highlighting the power of storytelling, and (iv) encouraging hope and a sense of belonging. The use of short animated film as a knowledge translation strategy can enhance our understanding, promote deep reflection, increase empathy and has the potential to lead to social change.
Testing Covariance Stationarity Under Moment Condition Failure with an Application to Common Stock Returns

This paper studies tests for covariance stationarity under conditions which permit failure in the existence of fourth order moments. The problem is important because many econometric diagnostics such as tests for parameter constancy, constant variance and ARCH and GARCH effects routinely rely on fourth moment conditions. Moreover, such tests have recently been extensively employed with financial and commodity market data, where fourth moment conditions may well be quite tenuous and are usually untested. This paper considers several tests for covariance stationarity including sample split prediction tests, cusum of squares tests and modified scaled range tests. When fourth moment conditions fail we show how the asymptotic theory for these tests involves functionals of an asymmetric stable Levy process, in place of conventional standard normal or Brownian bridge asymptotics. An interesting outcome of the new asymptotics is that the power of these tests depends critically on the tail thickness in the data. Thus, for data with no finite second moment, the above mentioned tests are inconsistent. Some new tests for heterogeneity are suggested that are consistent in the infinite variance case. These are easily implemented and rely on standard normal asymptotics. A consistent estimator of the maximal moment exponent of a distribution is also proposed. Again this estimator is easily implemented, has standard normal asymptotics and leads to a simple test for the existence of moments up to a given order. An empirical application of these methods to the monthly stock return data recently studied in Pagan and Schwert (1989a, 1989b) and the daily returns of the Standard and Poors 500 stock index is presented.
Strengths and weaknesses of qualitative research in social science studies

This study examines the strengths and limitations of 22 published journal articles by conducting a systematic literature review. The review revealed that the qualitative approach was flexible, offered in-depth and detailed information, allowed the use of multiple data collection methods and minimised the chance of having missing data. The approach was found (i) to integrate human touch, was cost-effective and was indeed the only option in some cases of research problems; (ii) to be prone to researchers’ subjectivity, involves complex data analysis, makes anonymity difficult and has limited scope in its generalizability, and (iii) to make replication of findings challenging, and the findings may be influenced by the researcher’s bias. It is concluded, therefore, that researchers should take necessary precautions when using the approach to ensure that the weaknesses of qualitative research do not bar them from achieving research objectives.
Antibacterial, Antioxidant, and Phytotoxic Potential of Phytosynthesized Silver Nanoparticles Using Elaeagnus umbellata Fruit Extract

Due to its eco-friendliness, cost-effectiveness, ability to be handled safely, and a wide variety of biological activities, the green plant-mediated synthesis of nanoparticles has become increasingly popular. The present work deals with the green synthesis and characterization of silver nanoparticles (AgNPs) using Elaeagnus umbellata (fruit) and the evaluation of its antibacterial, antioxidant, and phytotoxic activities. For the synthesis of AgNPs, fruit extract was treated with a 4 mM AgNO3 solution at room temperature, and a color change was observed. In UV-Visible spectroscopy, an absorption peak formation at 456 nm was the sign that AgNPs were present in the reaction solution. Scanning electron microscopy and physicochemical X-ray diffraction were used to characterize AgNPs, which revealed that they were crystalline, spherical, and had an average size of 11.94 ± 7.325 nm. The synthesized AgNPs showed excellent antibacterial activity against Klebsiella pneumoniae (14 mm), Staphylococcus aureus (13.5 mm), Proteus mirabilis (13 mm), and Pseudomonas aeruginosa (12.5 mm), as well as considerable antioxidant activity against DPPH with 69% inhibition at an IC50 value of 43.38 µg/mL. AgNPs also exhibited a concentration-dependent effect on rice plants. Root and shoot length were found to be positively impacted at all concentrations, i.e., 12.5 µg/mL, 25 µg/mL, 50 µg/mL, and 100 µg/mL. Among these concentrations, the 50 µg/mL concentration of AgNPs was found to be most effective. The plant biomass decreased at higher AgNP exposure levels (i.e., 100 µg/mL), whereas 50 µg/mL caused a significant increase in plant biomass as compared to the control. This study provides an eco-friendly method for the synthesis of AgNPs which can be used for their antibacterial and antioxidant activities and also as growth promoters of crop plants.
Body Image Dissatisfaction and Aggressive Behavior Among Chinese Children at Different Pubdertal Stages: A Path Analysis

Background Body image dissatisfaction and aggressive behavior have become important public health problem in children and adolescents, and body image dissatisfaction may increase the occurrence of aggressive behavior. The aim of this study was to explore the correlation between body image dissatisfaction and aggressive behavior among Chinese children in different developmental stages. Methods The stratified cluster sampling method was used to effectively survey 518 children aged 8–15 years. The Body Shape Questionnaire and the Buss-Warren Aggressive Questionnaire scale were used to measure body image dissatisfaction and aggressive behavior. Pubertal development stages were divided into three stages according to Tanner criteria. Results There was a main correlation path of body image dissatisfaction - hostility - anger - indirect aggression - physical aggression in boys with stage I and stage II and in girls with stage I and stage III. In addition, there were direct paths of hostility - indirect aggression, hostility - verbal aggression, anger - physical aggression, and anger - verbal aggression in boys with stage I; hostility - indirect aggression, hostility - verbal aggression, and anger - physical aggression in boys with stage II and in girls with stage I; and anger - physical aggression, and anger - verbal aggression in girls with stage III. Conclusion Body image dissatisfaction might positively correlate with aggression through hostility among Chinese children and adolescents, and their association paths were different in different puberty stages.
Experimental Comparison of Pre-Trained Word Embedding Vectors of Word2Vec, Glove, FastText for Word Level Semantic Text Similarity Measurement in Turkish

This study aims to evaluate experimentally the word vectors produced by three widely used embedding meth ods for the word-level semantic text similarity in Turkish. Three benchmark datasets SimTurk, AnlamVer, and RG65_Turkce are used in this study to evaluate the word embedding vectors produced by three different meth ods namely Word2Vec, Glove, and FastText. As a result of the comparative analysis, Turkish word vectors pro duced with Glove and FastText gained better correlation in the word level semantic similarity. It is also found that The Turkish word coverage of FastText is ahead of the other two methods because the limited number of Out of Vocabulary (OOV) words have been observed in the experiments conducted for FastText. Another ob servation is that FastText and Glove vectors showed great success in terms of Spearman correlation value in the SimTurk and AnlamVer datasets both of which are purely prepared and evaluated by local Turkish individuals. This is another indicator showing that these aforementioned datasets are better representing the Turkish lan guage in terms of morphology and inflections.
Association between cause of kidney failure and fracture incidence in a national US dialysis population cohort study

ABSTRACT Background Whether fracture rates, overall and by fracture site, vary by cause of kidney failure in patients receiving dialysis is unknown. Methods Using the US Renal Data System, we compared fracture rates across seven causes of kidney failure in patients who started dialysis between 1997 and 2014. We computed unadjusted and multivariable adjusted proportional sub-distribution hazard models, with fracture events (overall, and by site) as the outcome and immunoglobulin A nephropathy as the reference group. Kidney transplantation and death were competing events. Results Among 491 496 individuals, with a median follow-up of 2.0 (25%, 75% range 0.9–3.9) years, 62 954 (12.8%) experienced at least one fracture. Patients with diabetic nephropathy, vasculitis or autosomal polycystic kidney disease (ADPKD) had the highest (50, 46 and 40 per 1000 person-years, respectively), and patient with lupus nephritis had the lowest (20 per 1000 person-years) fracture rates. After multivariable adjustment, diabetic nephropathy [hazard ratio (HR) 1.43, 95% confidence interval 1.33–1.53], ADPKD (HR 1.37, 1.26–1.48), vasculitis (HR 1.22, 1.09–1.34), membranous nephropathy (HR 1.16, 1.02–1.30) and focal segmental glomerulosclerosis (FSGS) (HR 1.13, 1.02–1.24) were associated with a significantly higher, and lupus nephritis with a significantly lower (HR 0.85, 0.71–0.98) fracture hazard. The hazards for upper extremity and lower leg fractures were significantly higher in diabetic nephropathy, ADPKD, FSGS and membranous nephropathy, while the hazard for vertebral fracture was significantly higher in vasculitis. Our findings were limited by the lack of data on medication use and whether fractures were traumatic or non-traumatic, among other factors. Conclusions Fracture risk, overall and by fracture site, varies by cause of end-stage kidney disease. Future work to determine underlying pathogenic mechanisms contributing to differential risks might inform more tailored treatment strategies. Our study was limited by lack of data regarding numerous potential confounders or mediators including medications and measures or bone biomarkers.
Characteristic Analysis of Relationship Between Precipitation and Turbulence in the Troposphere and Lower Stratosphere Over Tropical Western Pacific

This study aims to analyze the distribution characteristics of turbulence in the western Pacific tropical region and determine the correlation between turbulence and precipitation. The obtained results show that due to the combined action of large wind shear and thermal convection, the near‐surface layer is conducive to the generation of large‐scale turbulence, and the maximum Thorpe scale can reach more than 600 m. A strong mixing zone of turbulence is generated in 10–16 km, with the monthly average occurrence rate of turbulence being about 40% and the maximum value reaching 50%. There is less turbulence in lower stratosphere due to the rapidly increased atmosphere static stability. All Thorpe scales in stratosphere are less than 100 m. The response of turbulence to precipitation in the upper troposphere is strongly affected by clouds. At the altitude in the clouds, the enhancement of precipitation has a negative relationship with turbulence. The average value of LT ${L}_{T}$ and turbulence layer thickness on rainy days are both smaller than those when the precipitation weakened. At the altitude above the clouds, the enhancement of precipitation has a slight promotion effect on the turbulence, especially for the emergence of large‐scale turbulence. When the precipitation intensifies, the amount of turbulence with LT ${L}_{T}$ greater than 150 m accounts for 6.7% of the total, while the proportion is only 2.6% on less rainy days.
Modeling, image processing and attitude estimation of high speed star sensors

Modeling, Image Processing and Attitude Estimation of High Speed Star Sensors. (August 2006) Anup Bharat Katake, B.Tech., Indian Institute of Technology, Bombay, India; M.S., Texas A&M University Chair of Advisory Committee: Dr. John L. Junkins Attitude estimation and angular velocity estimation are the most critical components of a spacecraft’s guidance, navigation and control. Usually, an array of tightlycoupled sensors (star trackers, gyroscopes, sun sensors, magnetometers) is used to estimate these quantities. The cost (financial, mass, power, time, human resources) for the integration of these separate sub-systems is a major deterrent towards realizing the goal of smaller, cheaper and faster to launch spacecrafts/satellites. In this work, we present a novel stellar imaging system that is capable of estimating attitude and angular velocities at true update rates of greater than 100Hz, thereby eliminating the need for a separate star tracker and gyroscope sub-systems. High image acquisition rates necessitate short integration times and large optical apertures, thereby adding mass and volume to the sensor. The proposed high speed sensor overcomes these difficulties by employing light amplification technologies coupled with fiber optics. To better understand the performance of the sensor, an electro-optical model of the sensor system is developed which is then used to design a high-fidelity night sky image simulator. Novel star position estimation algorithms based on a two-dimensional Gaussian fitting to the star pixel intensity profiles are then presented. These algorithms are non-iterative, perform local background estimation in the vicinity of the star and lead to significant improvements in the star centroid determination. Further, a new attitude determination algorithm is devel-