Miracle-dz/UniFAR-FTUs huggingface dataset:
Stabilized finite element method for heat transfer and turbulent flows inside industrial furnaces

The development of efficient methods to understand and simulate conjugate heat transfer for multi-components systems appears in numerous engineering applications and still a need for industrials, especially in the case of the heat treatment of high-alloy steel by a continuously heating process inside industrial furnaces. The thermal history of the load and the temperature distribution in the furnace are critical for the final microstructure and the mechanical properties of the treated workpieces and can directly determined their final quality in terms of hardness, toughness and resistance. The main objectives of this thesis is then to understand and better model the heat treatment process at the same time in the furnace chamber and within the workpieces under specified furnace geometry, thermal schedule, parts loading design, initial operation conditions, and performance requirements. The Computational Fluid Dynamics (CFD) simulation provides a useful tool to predict the temperature evolution and such processes. In the first part of this work, various stabilized finite element methods required for computing the conjugate heat transfer and the incompressible flows are proposed and analyzed. Two turbulence models, the k-epsilon and the Large Eddy Simulations (LES) models were introduced and used to simulate and take into account the complex turbulent flows inside the furnace chamber. The effect of thermal radiation was appropriately accounted for by means of a volumetric model known as the P1-model. In the latter part of this work, a multidomain approach referred as the immersed volume method (IVM) is introduced and applied to treat the fluid-solid interactions. It is based on the use of an adaptive anisotropic local grid refinement by means of the level-set function to well capture the sharp discontinuities of the fluid-solid interface. The proposed method showed that it is well suited to treat simultaneously the three modes, convective, conductive and radiative heat transfer that may interfere in both the fluid part and the solid part using anisotropic finite element meshes.
Amyloid Peptide β1-42 Induces Integrin αIIbβ3 Activation, Platelet Adhesion, and Thrombus Formation in a NADPH Oxidase-Dependent Manner

The progression of Alzheimer's dementia is associated with neurovasculature impairment, which includes inflammation, microthromboses, and reduced cerebral blood flow. Here, we investigate the effects of β amyloid peptides on the function of platelets, the cells driving haemostasis. Amyloid peptide β1-42 (Aβ1-42), Aβ1-40, and Aβ25-35 were tested in static adhesion experiments, and it was found that platelets preferentially adhere to Aβ1-42 compared to other Aβ peptides. In addition, significant platelet spreading was observed over Aβ1-42, while Aβ1-40, Aβ25-35, and the scAβ1-42 control did not seem to induce any platelet spreading, which suggested that only Aβ1-42 activates platelet signalling in our experimental conditions. Aβ1-42 also induced significant platelet adhesion and thrombus formation in whole blood under venous flow condition, while other Aβ peptides did not. The molecular mechanism of Aβ1-42 was investigated by flow cytometry, which revealed that this peptide induces a significant activation of integrin αIIbβ3, but does not induce platelet degranulation (as measured by P-selectin membrane translocation). Finally, Aβ1-42 treatment of human platelets led to detectable levels of protein kinase C (PKC) activation and tyrosine phosphorylation, which are hallmarks of platelet signalling. Interestingly, the NADPH oxidase (NOX) inhibitor VAS2870 completely abolished Aβ1-42-dependent platelet adhesion in static conditions, thrombus formation in physiological flow conditions, integrin αIIbβ3 activation, and tyrosine- and PKC-dependent platelet signalling. In summary, this study highlights the importance of NOXs in the activation of platelets in response to amyloid peptide β1-42. The molecular mechanisms described in this manuscript may play an important role in the neurovascular impairment observed in Alzheimer's patients.
TO STUDY THE PERCEPTION OF BUSINESS OWNERS CONSIDERING SOCIAL MEDIA A SALES TOOL

— Social Media is being in charge of all the industrial or non-industrial activities and content authorized as the king of those Social Media platforms. This paper is an attempt to study the perception of a business owner towards social media and their behavior toward social media marketing. All the data collected for this paper are the evaluation of the experience a business owner has while dealing their business using any social media platform in this era of fermentation in E-commerce activities. This paper discusses the idea of using social media as a sales tool and it also draws attention to consequences faced by a business owner while dealing with social media.
A real-time detection USV algorithm based on bounding box regression

With the rapid development of unmanned surface vessels (USV) applications, USV are widely used in military intelligence collection, target monitoring, and shipping services. However, this also brings security issues to shipping and the country. For more efficient detection of USV, in this paper, an enclosing center distance Intersection over Union (E-CIoU) algorithm is proposed for real-time USV detection, where the normalized distance between center points of the smallest enclosing box and target box is combined with bounding box regression. Meanwhile, we designed a new USV data set. The extensive experiments on USV data set have demonstrated that the proposed approach has achieved better convergence speed during training and a significant accuracy prediction.
MetaMap, an interactive webtool for the exploration of metatranscriptomic reads in human disease-related RNA-seq data

Motivation The MetaMap resource contains metatranscriptomic expression data from screening >17,000 RNA-seq samples from >400 archived human disease-related studies for viral and microbial reads, so-called “metafeatures”. However, navigating this set of large and heterogeneous data is challenging, especially for researchers without bioinformatic expertise. Therefore, a user-friendly interface is needed that allows users to visualize and statistically analyse the data. Results We developed an interactive frontend to facilitate the exploration of the MetaMap resource. The webtool allows users to query the resource by searching study abstracts for keywords or browsing expression patterns for specific metafeatures. Moreover, users can manually define sample groupings or use the existing annotation for downstream analysis. The web tool provides a large variety of analyses and visualizations including dimension reduction, differential abundance analysis and Krona visualizations. The MetaMap webtool represents a valuable resource for hypothesis generation regarding the impact of the microbiome in human disease. Availability The presented web tool can be accessed at https://github.com/theislab/MetaMap
Fabrication of Highly Stable Non-Volatile Memory Device Using Plasma-Polymerisation of Hexamethyldisiloxane with Graphene Quantum Dots

We demonstrated the fabrication of a highly stable non-volatile memory (NVM) device using pulsed radiofrequency (rf) plasma polymerization and a simple solution route. The two-terminal NVM devices were fabricated based on a metal-insulator-metal (MIM) structure consisting of graphene quantum dots (GQDs) embedded in hexamethyldisiloxane (HMDSO) dielectric layers. GQDs, the charge trapping layer, and the top contacts were formed by spin coating and spray coating methods. Whereas, the dielectric layers were deposited using pulsed rf plasma polymerization as it is a no thermal stressed induced method to deposit the insulator layers and does not disturb the deposited charge trapping nanoparticles. The current-voltage (I-V) curves showed highly reproducible bistable current behavior with the presence of a hysteresis window when measured at room temperature. The fabricated NVM memory devices were reprogrammable for multiple times with a distinct ON/OFF ratio of 103. Various conduction mechanisms were proposed based on the obtained I-V data.
Teaching Magneto-Thermal Coupling Using Thomson's Levitating Ring Experiment

The levitating ring experiment is presented as a method for teaching magneto-thermal interactions. The complete electromagnetic model of a problem is given, together with the insight in thermal analysis. The factors for determining the vertical displacements are explained, and an elegant method for indirect measurement of induced current in the ring is introduced. The whole apparatus is explained in detail so an accurate computer model can be made. Several simulation approaches are given, and all prove the applicability in teaching coupled problems using laboratory experiments and computer modeling.
ULF waves registered with the Ekaterinburg radar: Statistical analysis and case studies

A midlatitude coherent decameter radar installed near Ekaterinburg, Russia (EKB radar) has been operating since 2012. It is aimed at monitoring dynamics of the ionosphere–magnetosphere system in Eastern Siberia. A special operation mode is used at the radar to study ULF wave activity: three adjacent beams oriented approximately along the magnetic meridian are surveyed with time resolution of 18 s each. A number of wave observation events registered with the radar was analyzed and discussed in several papers. An overview of the main results from these studies is presented here.
Examining Effectiveness of an Authentic Problem-based Learning Model based on Uncertainty Level and Learning Performance of Engineering Students Studying Physics

: Introducing uncertainty into instruction-based classroom has been looked into by some educators to increase students’ learning in terms of problem solvi ng skills. In Problem-based learning (PBL) environment, uncertainty is naturally incorporated into the ill-structured problem which is crafted purposively to motivate and stimulate PBL learners’ learning process as the natural provocation for real learning. This study adopts the quasi-experimental approach and correlational design to explore the relationship between PBL learners’ uncertainty level and learning performance and the effectiveness of an authentic problem-based learning (APBL) model in reducing uncertainty level. 78 Physics students from Taylor’s American Degree Program in Fall 2017 semester participated in this study. A 30-item self-reporting, numerically measurable questionnaire to capture t he learners’ uncertainty level with regards to the cognitive, affective and physical dimension was developed. A pre-test on the uncertainty level was conducted to some students who have volunteered to fill out the questionnaire immediately after they learned the PBL scenario which served to determine whether the PBL question has incorporated uncertainty and if there is a reduce in uncertainty level after completing the PBL activities. Posttest on the uncertainty level was conducted after learners presented their solution or proposal to the problem. Learning performance scores constitutes three measures, namely learning satisfaction, learning attitude, and learning score. Learning satisfaction consists of 10-item self-reporting, numerically measurable questionnaire. The Cronbach’s alphas for uncertainty constru ct and learning satisfaction are 0.89 and 0.92 respectively. One sample T test was conducted to study the effectiveness of APBL in reducing the level of uncertainty. A Pearson’s correlation coefficient between the variables were obtained. The results of zero order correlation analysis showed a strong negative correlation between uncertainty level and learning performance. One sample t-test result showed that learners’ uncertainty level were significantly reduced after the APBL activities.
Exploring Indian wheat genotypes for less Celiac disease toxic epitopes

Wheat is an important cereal having significant role in food and nutritional security of large part of human population in the world. Large numbers of end-products are be made from wheat because of the presence of unique proteins which when mixed with water form visco-elastic complex called gluten. However, around 1% of world population suffers from celiac disease (CD) worldwide whichvaries with sex, age and location.The disease occurs in individuals with specific genetic background (with HLA-DQ2 and DQ8)whichare intolerant to the gluten proteins (Gliadins and Glutenins) of not only wheat but also to the prolamins fractions of barley (hordiens), rye (secalins) and oats (avenins)(McAllister et al.,2019). An autoimmune response starts when these proteins bind to the T-cells of the susceptible individuals. As a result, the villi in the small intestine get flattened and the surface area for the nutrient absorption is highly reduced and leads to malnutrition and other gastrointestinal problems.No pharmacological treatment is available to gluten-intolerant patients, and a strict, life-long gluten-free diet is the only safe and efficient treatment available.A diet is called gluten free only if the gluten content is less than 20ppm.
On Feature Selection Stability and Privacy Preserving Data Mining: A Data Perspective

The data relating to data mining has turned out to be profoundly multi-dimensional in the recent past. It is also to be noted that such dimensionality has rapidly expanded over time. Moreover, in light of the positive assessment norms for enhanced data mining concerts, feature selection opts for a petite subset of the significant features from the original dataset. The stability of the feature selection is a key criterion in feature selection algorithms. Moreover, the most important aspect is its sturdiness in reducing the disturbances in the training data or in the expansion of the most recent samples. Lately, it has been demonstrated that the stability of the feature selection usually centers on data, and that it is not entirely unbiased in terms of algorithm. The privacy-preserving data mining changes a portion of the sensitive and quasi-identifying attributes in order to keep the conceivable re-identification of an individual’s tuple through intrusive or malignant data miner and brings a choppy privacy conserved dataset. Since the stability of the feature selection relies primarily upon data, the stability of the feature selection lessens with such privacy-preserved choppy datasets. Besides, the privacy preserving ruffling associates stresses the stability of the selection of features and data utility. Picking proper privacy-preserving data mining technique with significant privacy-preserving ruffling to enhance feature selection stability alongside the greater privacy-preservation and data utility is consequently a challenging issue in the field of research. Hence, the present paper intends to highlight the issue with reference to the three algorithms for privacy-preserving data mining and their relative analysis.
COMPARSION BETWEEN PPO FROM PLANT SOURCES AND DIFFER-ENT CHEMICALS IN TATTOO DYES DECOLORIZATION

This study was aimed to measure the decolorization of tattoo dyes by different chemicals and polyphenol oxidases from several plant sources. The tattoo inks removal market has burgeoned over the years, due to increased spread of tattooed persons about the world. Laser and surgery are presently the gold standards for removing of the tattoo. However, both of them have blemishes. Consequently, lots of persons were preferring easier, faster and cheaper procedures for tattoo remove. In this study polyphenol oxidases enzyme from many plant sources and different chemicals were used for decolorization of tattoo dyes in vitro. The polyphenol oxidase enzyme was used for removing of tattoo dyes (brown and blue) in order to demonstrate their potential in the treatment and decolorization of the tattoo, which is hazardous when removing by laser. The results show that 89 and 82 % of the brown and blue tattoo dyes respectively, were removed after 24 hours by enzyme extracted from Malva parviflora leaves, whereas the decolorization efficiency of polyphenol oxidase from other plant sources given less than 16% of the same dyes. The results for tattoo dyes decolorization by different chemicals revealed that Bimethylbenzylamine was the best chemical used with decolorization ratio 36 and 38 % for brown and blue tattoo dye, respectively.
Research on Image Quality Evaluation Method of Depth Learning Model Based on Core Concept

In view of the fact that the existing image quality evaluation methods are generally artificial design features, it is difficult to automatically and effectively extract image features that conform to the human visual system. Inspired by human visual characteristics, a new full reference image quality evaluation method based on depth learning model based on core concepts is proposed. Firstly, depth learning algorithm is used to extract multi-layer features from reference images and distorted images respectively. Then, the local similarity of the feature map of the reference image and the distorted image in each layer is calculated as the local quality description of the corresponding depth. Finally, the local quality of all layers is synthesized to obtain the overall quality score of the image. On the basis of the pre-training model, the depth model network is fine-tuned by using the image visual evaluation data set to obtain a depth model for evaluation. The standard experiment shows that fine-tuning training of each pre-training model on the standard data set achieves good classification results. Experiments show that the designed depth learning model based on core concepts is superior to the existing full reference image quality evaluation methods, and its prediction results have good accuracy and consistency with subjective quality evaluation.
A low cost solution for RFID sensing

Recent years have witnessed a surge of wireless and mobile devices and their sensing technologies. Among these wireless technologies, RFID (Radio Frequency IDentificaion) sensing has attracted wide attention in both academia and industry due to its innovative applications including logistics, localization and tracking, health care, human-computer interaction, etc. The RFID sensing technologies can be roughly divided into two categories: (1) Contact-based sensing and (2) Contact-free sensing. In contact-based sensing, by attaching lightweight RFID tags on target objects, the movement of objects can be tracked by the movement of the tags. In contact-free scenarios, the tags do not physically contact to the objects. Through the analysis of backscatter signals of deployed RFID tags, the movement of objects can still be inferred since the movement of objects results in the change of backscatter signals (i.e., RSSI, phase, etc.). This thesis studies RFID sensing technology with physical layer information and explores the possibility of implementing novel applications using COTS (commercial off-the-shelf) RFID systems in both contact and contact-free manners. While the cost of an individual RFID tag can be kept very low, COTS RFID readers still incur very high cost due to non-trivial RF implementations. Therefore, we develop an innovative low-cost RFID solution to reduce the cost of RFID sensing technology for general purpose. The main contents of this thesis are follows. First, we study the problem of using the commodity RFID system to monitor human respiration in contact-based manner. Respiration monitoring helps to assess general personal health and gives clues to chronic diseases. Yet current respiration monitoring technologies are inconvenient and intrusive. For instance, typical respiration monitoring devices need to attach nasal probes or chest bands to users.
Genome-wide identification and expression analysis of the FAR gene family in hexaploid wheat (Triticum aestivum L.)

Fatty acyl-CoA reductase (FAR) is involved in the biosynthesis of primary alcohols, which are waxy constituents that play an important role in plant stress. Previous studies have shown that primary alcohol is the most important component in the wheat seeding stage and accounts for more than 80% of the total composition. To date, eight FAR genes have been identified in wheat, but there has not been a systematic analysis. In this study, a comprehensive overview of the TaFAR gene family was performed, including analyses of the phylogenetic relationship, the multiple sequence alignment, the conserved motif distribution and the expression pattern. The result showed that a total of 41 wheat FAR genes were identified and designated TaFAR1-A–TaFAR22-D; all FAR genes were divided into six classes according to their phylogenetic relationship, and most of the FAR genes might be related to wheat cuticular wax synthesis. The analysis of the promoter binding site showed that TaFAR genes could be regulated by the MYB transcription factor and could be used as target genes for hormone regulation under adverse conditions, especially during a drought. This study provides a basis for further analyses of the TaFAR gene function and of upstream regulatory genes.
Internet of Things based Driver Exhaustion Detection System using Distributed Sensor Network

Accidents involving vehicles occur almost every day. Most of the accidents are caused due to exhaustion of driver’s consciousness but this cause goes almost unreported due to the absence of systems to detect the fatigue. Techniques to measure driver fatigue can be invasive or non-invasive. All in all, driver-related information uncover a more grounded relationship to the driver's carefulness level than vehicle-related ones, which experience the ill effects of a bigger affectability to outer parameters, for example, street and traffic conditions. A measure of the amount of grip that the driver applies on the steering wheel is a more direct and non-invasive measure of his fatigue. An alcohol sensor when added to this system can further be used to check if the variation in grip is due to the effect of alcohol. A suitable algorithm is developed to confirm the occurrence of driver fatigue. The GSM and the GPS units attached to the system can be useful in notifying the control room in the event of driver fatigue. This project can be very useful in detecting fatigues in drivers associated with vehicle fleets using IoT.
MODEL SELECTION IN BIVARIATE REGRESSION MODELS

This study is on model selection in bivariate regression models. Data for this study were collected in CBN Annual Report (various issues), CBN Statistical bulletin (various issues)from 1990 to 2019, which consists of international oil prices (response variable) and unemployment rate (independent variable). Eight regression models; Linear Regression, Quadratic Regression, Cubic Regression, Power Regression, ab-Exponential Regression, Logarithmic Regression, Hyperbolic Regression and Exponential Regression were examined in this study. Five model selection techniques known as; coefficient of determination, standard error of Regression, Akaike Information Criterion, Schwarz Information Criterion, and Hannan-Quinn Information Criterion were used to select the best model. From the analysis, in the overall goodness of fit assessment, the study concluded that the ab- Exponential regression model with Exponential regression performs far better than the other six bivariate regression models employed in this study. Therefore, future researchers should look at a similar work by incorporating other nonlinear bivariate regression models like compound, growth and inverse Regression models to compare results.