Miracle-dz/UniFAR-FTUs huggingface dataset:
Research and application of rockburst intensity prediction model based on entropy coefficient and ideal point method

Based on the ideal point method,a model for predicting and classifying rockbursts was established. Considering the key affecting factors of rockburst comprehensively,three factors were selected as evaluation indexes including the ratio of maximum tangential stress and rock uniaxial compressive strength,the ratio of compressive and tensile strength of rock and elastic energy index. The weight coefficients of these evaluation indexes were infirmed by the information entropy theory. Finally,the model of rockburst classification based on the entropy and the ideal point method was applied to Cangling Tunnel. The research shows that the prediction results are in good agreement with practical situation of rockburst and the prediction model is reliable.
Reynolds and Favre-averaged rapid distortion theory for compressible, ideal-gas turbulence

Reynolds and Favre-Averaged Rapid Distortion Theory for Compressible, Ideal-Gas Turbulence. (May 2007) Tucker Alan Lavin, B.S., Texas A&M University Chair of Advisory Committee: Dr. Sharath S. Girimaji Compressible ideal-gas turbulence subjected to homogeneous shear is investigated at the rapid distortion limit. Specific issues addressed are (i) the interaction between kinetic and internal energies and role of pressure-dilatation; (ii) the modifications to pressure-strain correlation and Reynolds stress anisotropy and (iii) the effect of the composition of velocity fluctuations (solenoidal vs. dilatational). Turbulence evolution is found to be strongly influenced by gradient Mach number, the initial solenoidal-to-dilatational ratio of the velocity field and the initial intensity of the thermodynamic fluctuations. The balance between the initial fluctuations in velocity and thermodynamic variables is also found to be very important. Any imbalance in the two fluctuating fields leads to high levels of pressure-dilatation and intense exchange. For a given initial condition, it is found that the interaction via the pressuredilatation term between the momentum and energy equations reaches a peak at an intermediate gradient Mach number. The energy exchange between internal and kinetic modes is negligible at very high or very low Mach number values due to lack of pressure dilatation. When present, the exchange exhibits oscillations even as the sum of the two energies evolves smoothly. The interaction between shear and solenoidal initial velocity field generates dilatational fluctuations; for some intermediate levels of shear Mach number dilatational fluctuations account for 20% of the total fluctuations. Similarly, the interaction between shear and initial dilatation produces solenoidal os-
Comparing Selective Masking Methods for Depression Detection in Social Media

Abstract Identifying those at risk for depression is a crucial issue and social media provides an excellent platform for examining the linguistic patterns of depressed individuals. A significant challenge in depression classification problems is ensuring that prediction models are not overly dependent on topic keywords (i.e., depression keywords) such that it fails to predict when such keywords are unavailable. One promising approach is masking—that is, by selectively masking various words and asking the model to predict the masked words, the model is forced to learn the inherent language patterns of depression. This study evaluates seven masking techniques. Moreover, predicting the masked words during the pre-training or fine-tuning phase was also examined. Last, six class imbalanced ratios were compared to determine the robustness of masked words selection methods. Key findings demonstrate that selective masking outperforms random masking in terms of F1-score. The most accurate and robust models are identified. Our research also indicates that reconstructing the masked words during the pre-training phase is more advantageous than during the fine-tuning phase. Further discussion and implications are discussed. This is the first study to comprehensively compare masked words selection methods, which has broad implications for the field of depression classification and general NLP. Our code can be found at: https://github.com/chanapapan/Depression-Detection.
Stachydrine, a Bioactive Equilibrist for Synephrine, Identified from Four Citrus Chinese Herbs

Four Chinese herbs from the Citrus genus, namely Aurantii Fructus Immaturus (Zhishi), Aurantii Fructus (Zhiqiao), Citri Reticulatae Pericarpium Viride (Qingpi) and Citri Reticulatae Pericarpium (Chenpi), are widely used for treating various cardiovascular and gastrointestinal diseases. Many ingredients have already been identified from these herbs, and their various bioactivities provide some interpretations for the pharmacological functions of these herbs. However, the complex functions of these herbs imply undisclosed cholinergic activity. To discover some ingredients with cholinergic activity and further clarify possible reasons for the complex pharmacological functions presented by these herbs, depending on the extended structure–activity relationships of cholinergic and anti-cholinergic agents, a simple method was established here for quickly discovering possible choline analogs using a specific TLC method, and then stachydrine and choline were first identified from these Citrus herb decoctions based on their NMR and HRMS data. After this, two TLC scanning (TLCS) methods were first established for the quantitative analyses of stachydrine and choline, and the contents of the two ingredients and synephrine in 39 samples were determined using the valid TLCS and HPLC methods, respectively. The results showed that the contents of stachydrine (3.04‰) were 2.4 times greater than those of synephrine (1.25‰) in Zhiqiao and about one-third to two-thirds of those of Zhishi, Qingpi and Chenpi. Simultaneously, the contents of stachydrine, choline and synephrine in these herbs present similar decreasing trends with the delay of harvest time; e.g., those of stachydrine decrease from 5.16‰ (Zhishi) to 3.04‰ (Zhike) and from 1.98‰ (Qingpi) to 1.68‰ (Chenpi). Differently, the contents of synephrine decrease the fastest, while those of stachydrine decrease the slowest. Based on these results, compared with the pharmacological activities and pharmacokinetics reported for stachydrine and synephrine, it is indicated that stachydrine can be considered as a bioactive equilibrist for synephrine, especially in the cardio-cerebrovascular protection from these citrus herbs. Additionally, the results confirmed that stachydrine plays an important role in the pharmacological functions of these citrus herbs, especially in dual-directionally regulating the uterus, and in various beneficial effects on the cardio-cerebrovascular system, kidneys and liver.
Galaxy cluster studies with weak lensing magnification & shear

Clusters of galaxies offer a unique window for studying the Universe on the largest scales. As the most massive gravitationally bound systems to have formed, they serve as probes of the large-scale distributions of dark matter, the underlying cosmology, and the complicated intracluster physics that characterizes the evolution of these massive systems. Gravitational lensing is the deflection of light coming from distant sources, by gravitational potentials along its path. Being sensitive to all mass regardless of type or dynamical state, lensing is a valuable tool for studying dark matter and characterizing galaxy clusters. In the weak lensing regime, the very slight apparent distortion of galaxy shapes is referred to as the shear, while the focusing and amplification of light is referred to as the magnification. The former has become a well-developed and robust technique in astronomy over the past decade, but the latter has been largely overlooked until now. The work embodied in this thesis includes the first-ever significant detection of magnification by galaxy groups, and the first comparison between masses measured with weak lensing magnification and shear (Chapter 2). This is followed by an application to an enormous sample of galaxy clusters, yielding ground-breaking signal-to-noise for magnification and an analysis of redshift-dependent systematic effects. This project also provides measurements of the cluster mass-richness scaling relation, and is a milestone in moving from magnification detection to useful science (Chapter 3). Finally, a comprehensive gravitational lensing shear analysis is performed on the previous cluster sample, allowing for a critical comparison between cluster masses measured with the independent techniques, as a function of both richness and redshift. These shear measurements also allow for important constraints on a new sample of galaxy clusters, including the distribution of cluster centroid offsets, the mass-richness relation, and cluster redshift evolution (Chapter 4). This thesis details unprecedented measurements using a new technique – weak lensing magnification – and comparisons with the well-studied shear approach. The final product exemplifies the promise of the new method for measuring galaxy cluster masses, and also points to likely issues that will need to be addressed in future experiments.
Targeted Genomic Profiling and Chemotherapy Outcomes in Grade 3 Gastro-Entero-Pancreatic Neuroendocrine Tumors (G3 GEP-NET)

Background: Grade 3 gastro-entero-pancreatic neuroendocrine tumors (G3 GEP-NET) are poorly characterized in terms of molecular features and response to treatments. Methods: Patients with G3 GEP-NET were included if they received capecitabine and temozolomide (CAPTEM) or oxaliplatin with either 5-fluorouracile (FOLFOX) or capecitabine (XELOX) as first-line treatment (chemotherapy cohort). G3 NET which successfully undergone next-generation sequencing (NGS) were included in the NGS cohort. Results: In total, 49 patients were included in the chemotherapy cohort: 15 received CAPTEM and 34 received FOLFOX/XELOX. Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were 42.9%, 9.0 months, and 33.6 months, respectively. Calculating a Ki67 cutoff using ROC curve analysis, tumors with Ki67 ≥ 40% had lower ORR (51.2% vs. 0%; p = 0.007) and shorter PFS (10.6 months vs. 4.4 months; p < 0.001) and OS (49.4 months vs. 10.0 months; p = 0.023). In patients who received FOLFOX/XELOX as a first-line treatment, ORR, PFS, and OS were 38.2%, 7.9 months, and 30.0 months, respectively. In the NGS cohort (N = 13), the most mutated genes were DAXX/ATRX (N = 5, 38%), MEN1 (N = 4, 31%), TP53 (N = 4, 31%), AKT1 (N = 2, 15%), and PIK3CA (N = 1, 8%). Conclusions: FOLFOX/XELOX chemotherapy is active as the first-line treatment of patients with G3 GEP-NET. The mutational landscape of G3 NET is more similar to well-differentiated NETs than NECs.
Islamic bank margins in Indonesia: The role of market power and bank-specific variables

Abstract Our study examines the determinants of bank margins in Indonesian Islamic banks. The determinants of bank margins consist of market-and-bank-specific variables. We investigate 31 banks, using quarterly data from 2015: Q1 to 2020: Q4. Panel regression with unbalanced data is employed. The findings indicate that higher Islamic bank margins are positively linked to banks with higher market power. Bank with high risk-sharing financing has low bank margins. Bank-specific variables such as income diversification, risk-averse, financing, and financing risk influence bank margins. This study also documents that the effect of market power on Islamic bank margins is more pronounced in Islamic bank subsidiaries, and lower bank margins through risk-sharing financing are more prominent in Islamic bank subsidiaries. These results suggest important policy implications that Islamic banks should focus on risk-sharing financing as a core business of Islamic banks because it can reduce the price of Islamic bank financing products as well as their intermediation costs.
Open discrete mappings with unbounded coefficient of quasi-conformality on Riemannian manifolds

The paper is concerned with problems at the intersection of the theory of spatial quasi-conformal mappings and the theory of Riemann surfaces. Theorems on the local behaviour of one class of open discrete mappings with unbounded coefficient of quasi-conformality, which map between arbitrary Riemannian manifolds, are obtained. Such mappings are also shown to extend to isolated points of the boundary of the domain. Some results on the local behaviour of Sobolev and Orlicz-Sobolev classes are obtained as an application. Bibliography: 52 titles.
The Small-Molecule Enhancers of Autophagy AUTEN-67 and -99 Delay Ageing in Drosophila Striated Muscle Cells

Autophagy (cellular self-degradation) plays a major role in maintaining the functional integrity (homeostasis) of essentially all eukaryotic cells. During the process, superfluous and damaged cellular constituents are delivered into the lysosomal compartment for enzymatic degradation. In humans, age-related defects in autophagy have been linked to the incidence of various age-associated degenerative pathologies (e.g., cancer, neurodegenerative diseases, diabetes, tissue atrophy and fibrosis, and immune deficiency) and accelerated ageing. Muscle mass decreases at detectable levels already in middle-aged patients, and this change can increase up to 30–50% at age 80. AUTEN-67 and -99, two small-molecule enhancers of autophagy with cytoprotective and anti-ageing effects have been previously identified and initially characterized. These compounds can increase the life span in wild-type and neurodegenerative model strains of the fruit fly Drosophila melanogaster. Adult flies were treated with these AUTEN molecules via feeding. Fluorescence and electron microscopy and Western blotting were used to assess the level of autophagy and cellular senescence. Flying tests were used to measure the locomotor ability of the treated animals at different ages. In the current study, the effects of AUTEN-67 and -99 were observed on striated muscle cells using the Drosophila indirect flight muscle (IFM) as a model. The two molecules were capable of inducing autophagy in IFM cells, thereby lowering the accumulation of protein aggregates and damaged mitochondria, both characterizing muscle ageing. Furthermore, the two molecules significantly improved the flying ability of treated animals. AUTEN-67 and -99 decrease the rate at which striated muscle cells age. These results may have a significant medical relevance that could be further examined in mammalian models.
Analisis Kinerja Perbankan Syariah di Indonesia : Studi Masa Pandemi Covid-19

The Covid-19 pandemic has spread to various countries, including Indonesia. As a result, almost all sectors experienced contractions, including Islamic banking. However, Islamic banking is considered quite capable of surviving the storm of impact based on the previous crises' empirical facts. The purpose of this study is to analyze how the performance of Islamic banking during the Covid-19 pandemic. This research method uses a descriptive approach with a sample of 14 Islamic Commercial Banks in Indonesia. Secondary data in the form of financial report panel data is the source of data for the January - July 2020 period, obtained from the monthly reports of each Islamic bank. The results showed that Islamic banking performance based on debt financing fluctuated, while the performance based on equity financing experienced growth. The performance of third party funds has fluctuated.
Molecular Characterization of Avian Rotaviruses F and G Detected in Brazilian Poultry Flocks

Avian rotaviruses (RVs) are important etiologic agents of gastroenteritis in birds. In general, avian RVs are understudied; consequently, there is a paucity of information regarding these viruses. Therefore, the characterization of these viral species is highly relevant because more robust information on genetic, epidemiologic, and evolutionary characteristics can clarify the importance of these infections, and inform efficient prevention and control measures. In this study, we describe partial genome characterizations of two avian RV species, RVF and RVG, detected in asymptomatic poultry flocks in Brazil. Complete or partial sequences of at least one of the genomic segments encoding VP1, VP2, VP4, VP6, VP7, NSP1, NSP4, NSP4, or NSP5 of 23 RVF and 3 RVG strains were obtained, and demonstrated that multiple variants of both RVF and RVG circulate among Brazilian poultry. In this study, new and important information regarding the genomic characteristics of RVF and RVG is described. In addition, the circulation of these viruses in the study region and the genetic variability of the strains detected are demonstrated. Thus, the data generated in this work should help in understanding the genetics and ecology of these viruses. Nonetheless, the availability of a greater number of sequences is necessary to advance the understanding of the evolution and zoonotic potential of these viruses.
Herbivory Damage Increased VOCs in Wild Relatives of Murtilla Plants Compared to Their First Offspring

Murtilla (Ugni molinae) is a shrub native to Chile that has undergone an incipient domestication process aimed at increasing its productivity. The reduction in intrinsic chemical defenses due to the domestication process has resulted in a decrease in the plant’s ability to defend itself against mechanical or insect damage. In response to this damage, plants release volatile organic compounds (VOCs) as a means of defense. To understand how domestication has impacted the production of VOCs in the first offspring of murtilla, we hypothesized that their levels would be reduced due to the induction of mechanical and herbivore damage. To test this hypothesis, we collected VOCs from four offspring ecotypes and three wild relatives of murtilla. We induced mechanical and herbivore damage in the plants and then enclosed them in a glass chamber, where we captured the VOCs. We identified 12 compounds using GC-MS. Our results showed that wild relative ecotypes had a higher VOC release rate of 624.6 µg/cm2/day. Herbivore damage was the treatment that produced the highest release of VOCs, with 439.3 µg/cm2/day in wild relatives. These findings suggest that herbivory triggers defenses through the emission of VOCs, and that domestication has influenced the production of these compounds in murtilla. Overall, this study contributes to bridging the gap in the incipient domestication history of murtilla and highlights the importance of considering the impact of domestication on a plant’s chemical defenses.
Exploring the Sensory Properties and Preferences of Fruit Wines Based on an Online Survey and Partial Projective Mapping

Non-grapefruits with unique sensory properties and potential health benefits provide added value to fruit wine production. This study aimed to explore consumers’ fruit wine preferences and descriptors for the varied fruit wines. First, 234 consumers participated in an online survey concerning their preferences for different wines (grape, blueberry, hawthorn, goji, Rosa roxburghii, and apricot). In addition, their attitudes towards general health interests, food neophobia, alcoholic drinks, and sweetness were collected. Grape wine and blueberry wine were the most favored wines, and goji wine was the least liked fruit wine sample. Moreover, 89 consumers were invited to evaluate 10 commercial fruit wines by using partial projective mapping based on appearance, aroma, and flavor (including taste and mouthfeel) to obtain a comprehensive sensory characterization. Multifactor analysis results showed that consumers could differentiate the fruit wines. Participants preferred fruit wines with “sweet”, “sour”, and “balanced fragrance”, whereas “bitter”, “astringent”, “deep appearance”, and “medicinal fragrance” were not preferred. Attitudes toward health, food neophobia, alcohol, and sweetness had less influence than taste and aroma (sensory attributes) on the preferences for fruit wine products. More frequent self-reported wine usage resulted in higher consumption frequency and liking ratings compared to non-users. Overall, the main factors influencing consumer preference for fruit wines were the sensory characteristics of the products, especially the taste.
Studies Regarding the Ensquared Energy of a Geostationary Hyperspectral Infrared Sounder

Compared with low Earth orbit (LEO) satellites, the high altitude from geostationary Earth orbit (GEO) satellites leads to increased diffraction effects on hyperspectral infrared (HIR) sounders, which reduces the ensquared energy (EE) within the satellites’ field of view (FOV) and increases the pseudo noise of the measurements. To help understand how the instrument performance is affected by EE for the Geostationary Extended Observations Sounder (GXS), a point spread function (PSF) is used to simulate the contribution of each location within and outside of an FOV ( $4\times $ 4 km at nadir). The PSF is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) airborne simulator (MAS) data with a spatial resolution of 50 m, to determine an appropriate EE for GXS. Although wavenumber dependent, an EE of 70% is recommended, which ensures that all GXS channels have pseudo noise less than the instrument specifications. Regardless of the EE value, the pseudo noise reduces the precision of the temperature and moisture sounding retrievals in the troposphere. Even with an EE of 70%, the pseudo noise slightly increases the root-mean-square error (RMSE) by 3%–4% for temperature and by 1%–4% for relative humidity. If an EE of 70% is difficult to meet, due to cost for example, a lower EE can be a good tradeoff with only a slight degradation in the sounding retrieval quality, which may be overcome with spatial averaging using the inverted cone method. An EE of 50% would lead to an RMSE increase of about 6% for temperature and 3%–6% for relative humidity.
A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images

Diabetic retinopathy (DR) is a common complication of diabetes mellitus, and retinal blood vessel damage can lead to vision loss and blindness if not recognized at an early stage. Manual DR detection using large fundus image data is time-consuming and error-prone. An effective automatic DR detection system can be significantly faster and potentially more accurate. This study aims to classify fundus images into five DR classes, using deep learning methods, with the highest possible accuracy and the lowest possible computational time. Three distinct DR datasets, APTOS, Messidor2, and IDRiD, are merged, resulting in 5,819 raw images. Before training the model, various image preprocessing techniques are applied to remove artifacts and noise from the images and improve their quality. Three augmentation techniques: geometric, photometric, and elastic deformation, are used to create a balanced dataset. A shallow convolutional neural network (CNN) is developed using three blocks of convolutional layers and maxpool layers with a categorical cross-entropy loss function, Adam optimizer, 0.0001 learning rate, and 64 batch size as a base model, and this is also employed to determine the best data augmentation method for further processing. A study to optimize the performance is then conducted by changing different components and hyperparameters of the base model, resulting in our proposed RetNet-10 model. Six cutting-edge models are employed for comparison. Our proposed RetNet-10 model performed the best, with a testing accuracy of 98.65%. MobileNetV2, VGG16, Xception, VGG19, InceptionV3 and ResNet50 achieved testing accuracies of 91.42%, 90.16%,89.57%, 88.21%, 87.68% and 87.23%, respectively. The model is also trained with several k values to assess its robustness. After image processing and data augmentation, using the combined dataset, and fine-tuning the base model, our proposed RetNet-10 model outperformed other automated methods for DR diagnosis.