Miracle-dz/UniFAR-FTUs huggingface dataset:
Paleomagnetic constraints on the Mesozoic drift of the Lhasa terrane (Tibet) from Gondwana to Eurasia

The Mesozoic plate tectonic history of Gondwana-derived crustal blocks of the Tibetan Plateau is hotly debated, but so far, paleomagnetic constraints quantifying their paleolatitude drift history remain sparse. Here, we compile existing data published mainly in Chinese literature and provide a new, high-quality, well-dated paleomagnetic pole from the ca. 180 Ma Sangri Group volcanic rocks of the Lhasa terrane that yields a paleolatitude of 3.7°S ± 3.4°. This new pole confirms a trend in the data that suggests that Lhasa drifted away from Gondwana in Late Triassic time, instead of Permian time as widely perceived. A total northward drift of ∼4500 km between ca. 220 and ca. 130 Ma yields an average south-north plate motion rate of 5 cm/yr. Our results are consistent with either an Indian or an Australian provenance of Lhasa.
Phosphoramidate Azole Oligonucleotides for Single Nucleotide Polymorphism Detection by PCR

Detection of the Kirsten rat sarcoma gene (KRAS) mutational status is an important factor for the treatment of various malignancies. The most common KRAS-activating mutations are caused by single-nucleotide mutations, which are usually determined by using PCR, using allele-specific DNA primers. Oligonucleotide primers with uncharged or partially charged internucleotide phosphate modification have proved their ability to increase the sensitivity and specificity of various single nucleotide mutation detection. To enhance the specificity of single nucleotide mutation detection, the novel oligonucleotides with four types of uncharged and partially charged internucleotide phosphates modification, phosphoramide benzoazole (PABA) oligonucleotides (PABAO), was used to prove the concept on the KRAS mutation model. The molecular effects of different types of site-specific PABA modification in a primer or a template on a synthesis of full-length elongation product and PCR efficiency were evaluated. The allele-specific PCR (AS-PCR) on plasmid templates showed a significant increase in analysis specificity without changes in Cq values compared with unmodified primer. PABA modification is a universal mismatch-like disturbance, which can be used for single nucleotide polymorphism discrimination for various applications. The molecular insights of the PABA site-specific modification in a primer and a template affect PCR, structural features of four types of PABAO in connection with AS-PCR results, and improvements of AS-PCR specificity support the further design of novel PCR platforms for various biological targets testing.
Stick-split mechanism for anthropogenic fluid-induced tensile rock failure

Fluids play a critical role in natural and human-induced rock failure. It is unclear, however, if propagation of a tensile fracture is inherently an episodic or continuous process. For example, typical average propagation speeds of hydraulic fracture tips on the order of 1–10 m/min suggest continuous crack growth, possibly at subcritical stress intensities. In contrast, using field observations and numerical and mathematical analyses, we show that fracture growth due to anthropogenic hydraulic fracturing is most likely to occur in an episodic fashion, characterized by stick-split behavior that is analogous to stick-slip motion of earthquakes. The stick-split mechanism is regulated by cyclic variations in fluid pressure near the crack tip, in which each successive failure produces a local pressure drop that temporarily halts or slows fracture propagation. A pressure drop results in partial fracture closure, producing noncontinuous fracture propagation through a process that is reminiscent of hand clapping. Rupture speeds for individual failure events are on the order of the shear-wave velocity of the medium; thus, continuous crack growth is not a likely mechanism for anthropogenic hydraulic fracturing treatments despite slow average tip propagation speeds.
A qualitative study of pain and related symptoms experienced by people with Ehlers-Danlos syndromes

Introduction Individuals with Ehlers-Danlos syndromes (EDS) often have complex and multi-faceted symptoms across the lifespan. Pain and the related symptoms of fatigue and sleep disorders are common. The objective of this qualitative study was to understand how participants manage their pain and related symptoms. Methods The design was a qualitative thematic content analysis. Twenty-eight interviews were conducted to collect data from individuals who were participants in a prior quantitative longitudinal study. A semi-structured interview guide was designed to focus on and understand the trajectory of pain, sleep, fatigue, and general function. The interview continued with questions about coping mechanisms and obstacles to maintaining a sense of well-being. Results Symptoms reported by participants were widespread and often interwoven. Pain was universal and often resulted in fatigue and disordered sleep which impacted physical function. Most participants reported that their symptoms worsened over time. Participants reported a wide range of effective interventions and most reported developing self-care strategies to adapt to their disabilities/limitations. Solutions included complementary interventions discovered when conventional medicine was unsuccessful. Very few relied on a “system” of health care and instead developed their own strategies to adapt to their disabilities/limitations. Discussion EDS symptoms are often debilitating, and their progression is unknown. For most participants, symptoms worsened over the time. Even though participants in our study, by experience, were self-reliant, the importance of knowledgeable medical providers to help guide self-care should be emphasized.
Determining the moment-rotation curve for top and seat angle and stiffened welded-seat connections using Chisala’s model

: This research aims to investigate the analytical procedure of constructing the moment-rotation curve for top and seat angle, and stiffened welded-seat connections using Chisala’s model. As these connections practically exhibit a semi-rigid flexural performance, the moment-rotation curve appears to provide useful information and helpful vision on their performance for design engineers. Since the process of determining the precise moment-rotation curve for a steel connection is costly due to the need for laboratory experiments or heavy finite element computations, using a precise and straightforward analytical model appears to be more feasible. Chisala’s model, having fewer and independent parameters, is precise and easy to construct, which is fit for the purpose. In this regard, the procedure of constructing the analytical model for both types of connections using Chisala’s model is explained, and the key parameters of Chisala’s model are determined. As a means of verification, finite element models of both types of connections were constructed as well, using Abaqus software. To ensure that the results are not accidental, a total of 34 TSA (Top and Seat Angle), and 33 SWS (Stiffened Welded-Seat) connection specimens with different geometric dimensions were tested, and the effects of geometric dimensions on the flexural performance of each type as side results were described. Based on the obtained data, analytical and numerical moment-rotation curves, for both types of connections, were illustrated. The results suggest that Chisala’s model can precisely predict the moment-rotation curve for the mentioned types of connections, provided that a proper equivalent analytical model is created
Investigating EFL Moroccan University Students’ Level of Intercultural Communication Competence: School of Arts and Humanities Meknes, Morocco.

The purpose of the present study was to investigate EFL Moroccan university students’ level of intercultural communication competence (ICC), which is the ability to communicate effectively across cultural interactions, given its importance in the English language teaching (ELT) field especially for graduates. In order to do so, a multi-sectioned questionnaire based on the three-level ICC model proposed by Byram (1997) was administered to a total of 30 conveniently selected EFL Moroccan university students, belonging to the sixth semester of the English major, in Moulay Smail University of Arts and Human Sciences. The questionnaire was designed to tackle students’ ICC knowledge, attitudes, and skills regarding their culture as well as other cultures all in hopes to discover their level of ICC and their readiness to utilize it successfully after graduation. The results revealed that the students possess enough knowledge of their culture as well as the surface-level and deep-level elements of other cultures. Moreover, the respondents displayed positive attitudes of acceptance, openness, appreciation, and respect showing no sign of ethnocentrism or negativity. The students also proved to be skillful when it comes to carrying out intercultural interactions with people from other cultures successfully.
Sharing and Interaction of Communication: A Study of Internet Group Polarization from the Perspective of Cultural Studies

: The decentralization and interactivity of Internet communication is the starting point for understanding the polarization of public opinion groups. The internet community is often hindered by various public opinions and disputes, which make rational discussion more challenging. The cultural theory of communication reveals that culture is the core to maintain the existence of a society, and communication is the foundation of culture. This paper examines the technical and interactive characteristics of Internet public opinion polarization from the perspective of cultural theory, and then focuses on the governance of Internet group polarization in a shared and interactive way from the perspective of cultural communication.
Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017-2022 Utilizing Advanced AI Models

Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.
Impacts of Sea Ice Thickness Initialization on Seasonal Arctic Sea Ice Predictions

AbstractA promising means for increasing skill of seasonal predictions of Arctic sea ice is improving sea ice thickness (SIT) initial conditions; however, sparse SIT observations limit this potential. Using the Canadian Climate Model, version 3 (CanCM3), three statistical models designed to estimate SIT fields for initialization in a real-time forecasting system are applied to initialize sea ice hindcasts over 1981–2012. Hindcast skill is assessed relative to two benchmark SIT initialization methods (SIT-IMs): a climatological initialization currently used operationally and SIT values from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Based on several measures of skill, sea ice predictions are generally improved relative to a climatological initialization. The accuracy with which the initialization fields represent both the thinning of the ice pack over time and interannual variability impacts predictive skill for pan-Arctic sea ice area (SIA) and regional sea ice concentration (SIC).
Alkaline cooking, preparation and quality of corn tortilla from Ecatlan, Puebla, Mexico

The varieties of maize obtained by breeding are generally inappropriate for peasants crop conditions; they prefer native breed of maize, organized in genetic groups (components), called varietal patterns, according to environmental conditions and uses. The knowledge of quality criteria used for obtaining native breed contributes to finding varieties acceptable for tortilla preparation. For those reasons, nixtamalization (cooking of maize with lime) and the processing of tortilla were studied with respect to quality in a community in the State of Puebla, México. Samples of native varieties were collected, and a group of local housewives was invited to prepare nixtamal (cooked maize) and make tortillas with their own maize variety (style-variety). There was great variation in most of the studied variables. Relationships between the components of the varietal pattern and the proportion of hardness and dough adhesiveness, as well as reflectance, and extending and cooking time of the tortilla were detected. The maize varieties of white grain showed high magnitudes, those of blue grain low, and the yellow ones had intermediate values.
Sparse Coding Based Feature Representation Method for Remote Sensing Images

SPARSE CODING BASED FEATURE REPRESENTATION METHOD FOR REMOTE SENSING IMAGES Ender Oguslu Old Dominion University, 2016 Director: Dr. Jiang Li In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft threshold function to obtain sparse feature representations for HSI. Experimental results showed that a randomly selected dictionary could be as effective as a dictionary learned from optimization. The new representation usually has a very high dimensionality requiring a lot of computational resources. In addition, the spatial information of the HSI data has not been included in the representation. Thus, we modify the framework by incorporating the spatial information of the HSI pixels and reducing the dimension of the new sparse representations. The enhanced model, called sparse coding based dense feature representation (SC-DFR), is integrated with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) classifiers to discriminate different types of land cover. We evaluated the proposed algorithm on three well known HSI datasets and compared our method to four recently developed classification methods:
Clinical Study Human Leukocyte Antigen Class II Alleles ( DQB 1 and DRB 1 ) as Predictors for Response to Interferon Therapy in HCV Genotype 4

1 Departments of Medical Biochemistry & Molecular Biology, Faculty of Medicine, Cairo University, Cairo, Egypt 2 Department of Zoology, Faculty of Science, Cairo University, Cairo 12613, Egypt 3 Departments of Tropical Medicine & Hepatology, Faculty of Medicine, Cairo University, Cairo, Egypt 4Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
Appraisal of a high protein distiller’s dried grain (DDG) in diets for European sea bass,Dicentrarchus labraxfingerlings on growth performance, haematological status and related gut histology

High protein distillers dried grains (HP-DDG) is a co-product of ethanol production that 27 uses pre-fractionation technology. An 8 week growth trial was conducted to investigate the effect 28 of partial replacement of soybean meal (SBM) by three levels of HP-DDG (30, 40 and 50%) on 29 growth performance, physiological parameters and histological changes of the intestine of 30 European seabass, Dicentrarchus labrax . The results indicated that an increased dietary level of 31 HP-DDG of more than 30% significantly increases growth performance and improved the FCR of 32 seabass. In addition, replacement of SBM by HP-DDG enhanced feed intake efficiency and the 33 health status of fish. Hematology and serum biochemistry (hemoglobin (Hb), red blood cells 34 (RBCs), white blood cells (WBCs), packed cell volume (PCV %) and humeral immune parameters 35 including total protein, albumin, globulin, cholesterol, lysozyme activity and total antioxidant 36 capacity significantly increased with increase HP-DDG inclusion levels. The findings of this study 37 indicated that HPDDG is a good complementary protein source for inclusion aquaculture diets and 38 levels above 30% as a replacement of SBM did not compromise growth performance and 39 physiological parameters of seabass whilst enhancing some important indices of health status in 40 this species 41 45 46 47 48 49 50
Identification of 1-Aminocyclopropane-1-Carboxilid Acid (ACC)-Deaminase Producing Endophytic Bacteria from Local Agricultural Plantation Based on 16S Ribosomal RNA Gene as Genetic Marker

The objective of this work was to isolate and identify of 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase producing endophytic bacteria from root of local agricultural plantation by using 16S rRNA gene as genetic marker. Twelve root samples were collected from local agricultural plantation in Java area. After surface sterilization, each root sample was inoculated in nutrient agar media and the grown colonies were further purified and tested for the ability to grow in N-free minimal medium containing AIB as a sole of nitrogen source. The selected colonies were further tested for their ACC deaminase activity by measuring the rate of conversion of ACC into α-ketobutyrate. From this work, 12 bacterial strains that exhibited the ACC deaminase activity by 123.75 to 1461.44 nmol α-ketobutyrate/mg/hour were successfully isolated. Based on the 16SrRNA gene sequences, those bacterial isolates were identified as Sphingobacterium multivorum BK1, Bacillus mycoides CB2, Pantoea dispersa CK4, Pantoea agglomerans KD6.2, Enterobacter ludwigii KW3, Bacillus aryabhattai TW7, Pseudomonas monteilii KS12, Pseudomonas plecoglossicida KS16.2, Pseudomonas putida PIR3C, Stenotrophomonas maltophilia PIR5, Lysinibacillus pakistanensis PIC5, and Raoultella terrigena PCM8. Pseudomonas putida PIR3C and Pseudomonas monteilii KS12 showed promising ACC deaminase activity and therefore it could be as a good candidate for further application in plant growth promoting in stress conditions.
Atmospheric benzene observations from oil and gas production in the Denver‐Julesburg Basin in July and August 2014

High time resolution measurements of volatile organic compounds (VOCs) were collected using a proton‐transfer‐reaction quadrupole mass spectrometry (PTR‐QMS) instrument at the Platteville Atmospheric Observatory (PAO) in Colorado to investigate how oil and natural gas (O&NG) development impacts air quality within the Wattenburg Gas Field (WGF) in the Denver‐Julesburg Basin. The measurements were carried out in July and August 2014 as part of NASA's “Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality” (DISCOVER‐AQ) field campaign. The PTR‐QMS data were supported by pressurized whole air canister samples and airborne vertical and horizontal surveys of VOCs. Unexpectedly high benzene mixing ratios were observed at PAO at ground level (mean benzene = 0.53 ppbv, maximum benzene = 29.3 ppbv), primarily at night (mean nighttime benzene = 0.73 ppbv). These high benzene levels were associated with southwesterly winds. The airborne measurements indicate that benzene originated from within the WGF, and typical source signatures detected in the canister samples implicate emissions from O&NG activities rather than urban vehicular emissions as primary benzene source. This conclusion is backed by a regional toluene‐to‐benzene ratio analysis which associated southerly flow with vehicular emissions from the Denver area. Weak benzene‐to‐CO correlations confirmed that traffic emissions were not responsible for the observed high benzene levels. Previous measurements at the Boulder Atmospheric Observatory (BAO) and our data obtained at PAO allow us to locate the source of benzene enhancements between the two atmospheric observatories. Fugitive emissions of benzene from O&NG operations in the Platteville area are discussed as the most likely causes of enhanced benzene levels at PAO.
Rapid Grapevine Health Diagnosis Based on Digital Imaging and Deep Learning

Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures.