Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of the Covid-19 Pandemic on the Risk-taking Behavior of Non-financial Firms Listed in Vietnam with Cash Holdings as a Moderating Variable

Vietnam has faced the long-lasting COVID-19 outbreak, which has imposed a negative impact on corporate activities, since 2020. This study aims to examine how the COVID-19 outbreak has influenced the risk-taking behavior of companies in Vietnam, investigating this relationship with the moderating role of cash holdings. We used the research data of non-financial listed firms in Vietnam during the period from quarter 1 2018 to quarter 2 2021. According to the Generalized Method of Moments approach, the study concludes that the COVID-19 pandemic period is negatively associated with corporate risk-taking behavior. In addition, there is a significant increase in the level of risk-taking behavior during the COVID-19 pandemic period by enterprises that have more cash holdings.
NUMERICAL SIMULATION STUDY ON BOUNDARY LAYER FLOW AND DRAG REDUCTION CHARACTERISTICS OF RIBLETS

Turbulent drag reduction is a hot issue in the research of fluid mechanics. Among various turbulent drag reduction methods, riblet is widely concerned due to its simple structure and convenient use. Although the drag reduction effect of riblet has been confirmed by a lot of experiments, there is no unified theory of the drag reduction mechanism of riblets due to limitations of experiment measurement methods. In this paper, large eddy simulation (LES) method is adopted to numerically study the effect of riblets on the turbulent boundary layer. The accuracy of the numerical result is verified by comparing it to the results of direct numerical simulation (DNS). Based on experiment results, flow field parameters of the turbulent boundary layer over riblets are analyzed, such as integral statistics, first-order statistics, and second-order statistics.
An Ontology for Intra-Campus Transport System (ICTS) (A Case Study of the University of Ibadan Campus)

Knowledge representation can greatly improve the effectiveness and efficiency of an organization, business or firm if its benefits are properly harnessed. Ontology is a knowledge representation technique that involves formalization of world knowledge. Existing transport-related ontologies have not really captured and represented the basics of a transport system. This has made it difficult for information systems and machines to access, understand and share basic knowledge about transport domain. The Intra-Campus Transport System (ICTS) which handles the transportation of people within an institution can therefore serve as a prototype for transport systems. This research work is therefore aimed at developing a formal ontology for the intra-campus transport system which enhances availability of knowledge and a quick retrieval system. A very thorough knowledge engineering approach is adopted for this research work. It involves knowledge gathering, also known as ontology capture which gives important factual knowledge that exists in the ICTS domain. This is followed by knowledge refining and analysis, which involves categorizing the gathered knowledge into classes, properties and individuals. Knowledge formalization is used to translate the refined knowledge into a logical form using description logic tools. Lastly, the logical axioms were implemented with web ontology language (OWL) format using Protégé 5.0.
Prospective Cohort Study of Sociodemographic and Work-Related Factors and Subsequent Unemployment under COVID-19 Pandemic

The previous studies found that women and low-income households were more likely to experience unemployment prior to the COVID-19 pandemic. However, there is no cohort study to examine the relationship during the COVID-19 pandemic. The aim of this prospective cohort study is to examine the relationship between sociodemographic factors and unemployment during the COVID-19 pandemic in Japan. We surveyed the socioeconomic status, personal characteristics, and occupation of recruited workers at baseline (22–25 December 2020); subsequent unemployment was examined at follow-up (18–19 February 2021). We determined the odds ratio of unemployment by sociodemographic status and occupation. The multivariate model was adjusted for sex and age. Among the 19,941 participants, 725 (3.6%) had experienced unemployment. Multivariate analysis showed significant high unemployment amongst women and participants of younger age, bereaved or divorced, unmarried, of lower income, or with short educational background. By occupation, the unemployment rate of temporary or contract employees and self-employed is high. COVID-19 expelled socially vulnerable groups from employment. This suggests the need for employment and economic support for such individuals.
BITCOIN PRICE PREDICTION USING SVM

: Bitcoin is one of the most valuable Crypto currency in the world with the prices as high as 68,078 United States Dollar (USD) in November of 2021. It made Bitcoin a very profitable market for investment but Bitcoin saw many ups and down as well. Prices of Bitcoin have highly fluctuated which make them very difficult to predict. Hence, this research aims to discover the most efficient and highest accuracy model to predict Bitcoin prices from various machine learning algorithms. By using 1-minute interval trading data on the Bitcoin exchange website named bitstamp some different regression models with Scikit-learn and Keras libraries had experimented. The dataset used contains minute by minute prices of Bitcoin of over 5 years and contains almost 3 Million entries. Since, the dataset used is a big data, evaluating the performance of algorithms over a large dataset will give accurate results.
Addition of the Aldose Reductase Inhibitor Benzofuroxane Derivative BF-5m to Prolonged and Moderate Exercise Training Enhanced Protection of the Rat Heart From Type-1 Diabetes

Moderate exercise training may not be sufficient to exert beneficial effects on the cardiovascular system because of the long-term multifactorial etiology of diabetic complications. The addition of a proper pharmacological tool to the physical exercise should improve the outcomes of the diabetic damage. Here it is shown that 8 weeks exercise training of type 1 diabetic Sprague-Dawley (SD) rats resulted in a significantly increased heart rate, a 14% increase in the left ventricular ejection fraction (LVEF) increased plasma insulin levels and a 13% decrease in plasma glucose with respect to sedentary animals. The training also resulted in a 22% reduction in cardiac QT interval from a diabetic sedentary value of 185 ± 19 ms. Treatment of trained rats with the new antioxidant and NO-releasing aldose reductase 2 inhibitor 5(6)-(benzo[d]thiazol-2-ylmethoxy) benzofuroxane BF-5m, 20 mg/kg/day, added a further and significant (P < 0.01 vs. sedentary) increase of the LVEF up to 38% at 8 week time point. The long QT interval recorded in trained rats was reduced to further 12% by addition to the training of pharmacological treatment with 20 mg/kg/day BF-5m. At this time, the association of the two treatments improved the expression into the cardiac tissue of sarcoplasmic reticulum Ca2+ ATPase 2 (SERCA2) and manganese superoxide dismutase (MnSOD), and reduced the fibrosis.
Manifold learning: an informal introduction

A manifold is a collection of (abstract) points with additional information about their relations. One way to think about manifolds is to consider them as objects in the real world: a piece of paper, a cell, the earth, or the water that is flowing in a river. These are all collections of points (atoms, molecules) with additional structure that is particular to them. A (flat) piece of paper is made of atoms, arranged in a particular way so that it can be distinguished from a ring-shaped object, for example. The ring can also be comprised purely of “paper molecules”, but would still not be “the same manifold” because it has a different shape–the relation between the points is different! An important concept for manifolds is their dimension. Intuitively, the dimension of a manifold is its local resemblance of Euclidean space. For a sheet of paper, this notion means that when we zoom into it at any point, the sheet has to look like a two-dimensional plane independent of its global shape, which might be crumpled up, folded, or stretched out. This means the sheet of paper is a two-dimensional manifold. If we look (not too closely) at a thin strand of
Nanocrystalline Electrodeposited Fe-W/Al2O3 Composites: Effect of Alumina Sub-microparticles on the Mechanical, Tribological, and Corrosion Properties

In this study, nanocrystalline Fe-W alloy and Fe-W/Al2O3 composite coatings with various contents of sub-microsized alumina particles have been obtained by electrodeposition from an environmentally friendly Fe(III)-based electrolyte with the aim to produce a novel corrosion and wear resistant material. The increase in volume fraction of Al2O3 in deposits from 2 to 12% leads to the grain refinement effect, so that the structure of the coatings change from nanocrystalline to amorphous-like with grain sizes below 20 nm. Nevertheless, the addition of particles to the Fe-W matrix does not prevent the development of a columnar structure revealed for all the types of studied coatings. The observed reduction in both hardness and elastic modulus of the Fe-W/Al2O3 composites is attributed to the apparent grain size refinement/amorphization and the nanoporosity surrounding the embedded Al2O3 particles. In the presence of 12 vol% of Al2O3 in deposits, the wear rate decreases by a factor of 10 as compared to Fe-W alloy tested under dry friction conditions due to the lowering of tribo-oxidation. The addition of alumina particles slightly increases the corrosion resistance of the coatings; however, the corrosion in neutral chloride solution occurs through the preferential dissolution of Fe from the matrix. The obtained results provide a possibility to integrate the nanocrystalline Fe-W/Al2O3 composite coatings into various systems working under dry friction conditions, for example, in high-temperature vacuum systems.
Large-scale Image Classification with Explainable deep learning scheme

: For the uncertainty and complexity in object decision making and the 9 differences of decision makers’ reliabilities, an object decision making method based 10 on deep learning theory is proposed. However, traditional deep learning approaches 11 optimize the parameters in an "end-to-end" mode by annotating large amounts of data 12 to propagate the errors backwards. The learning method could be considered to be as a 13 "black box", which is weak in explainability. Explainability refers to an algorithm that 14 gives a clear summary of a particular task and connects it to defined principles or 15 principles in the human world. This paper proposes an explainable attention model 16 consisting of channel attention module and spatial attention module. The proposed 17 module derives attention graphs from channel dimension and spatial dimension 18 respectively, then the input features are selectively learned according to the 19 importance of the features. For different channels, the higher the weight, the higher 20 the correlation which required more attention. The main function of spatial attention 21 is to capture the most informative part in the local feature graph, which is a 22 supplement to channel attention. We evaluate our proposed module based on the 23 ImageNet-1K and Cifar-100 respectively. Experimental results show that our
High Resolution Imaging with SAR using Automotive Millimeter Wave Radars

Angular resolution is currently one of the main limitations in automotive radars used in advanced driver assistance systems (ADAS). This thesis investigates the possibility of achieving fine-resolution radar images with a commercial, off-the-shelf automotive radar sensor using synthetic aperture radar (SAR). Two imaging algorithms have been investigated and implemented with a Texas Instruments AWR1843 radar system, and the resulting resolution in azimuth has been compared to that of conventional processing methods. The potential of a side-looking SAR imaging system has been investigated in theory, in simulations and an experimental system was implemented on an actual moving vehicle. Imaging of various automotive scenarios have been performed, and the image quality of the two algorithms, backprojection and Doppler beam sharpening (DBS), has been evaluated. Simulations and measurements show that the resulting system greatly improves azimuth resolution over the conventional multiple-input multipleoutput (MIMO) imaging capability of the AWR1843 when in a side-looking mode of operation. The results have helped uncover potential challenges with SAR imaging, such as dynamic scenes and imperfect radar platform motion estimation. The latter problem was overcome using autofocus algorithms. In conclusion, the devised SAR system shows great promise in delivering fine azimuthal information for improving situational awareness in future ADAS functionality using today’s hardware.
Generalized Galois Logics: Relational Semantics of Nonclassical Logical Calculi

Nonclassical logics have recently played an increasing role in disciplines ranging from mathematics and computer science to linguistics and philosophy. "Generalized Galois Logics" develops a uniform framework of relational semantics to mediate between logical calculi and their semantics through algebra. This volume addresses normal modal logics such as K and S5, and substructural logics, including relevance logics, linear logic, and Lambek calculi. The authors also treat less-familiar and new logical systems with equal deftness.
An alternative ranking system for biathlon pursuit races

Biathlon is an Olympic sport combining cross-country skiing with rifle shooting, giving a penalty for each target miss. The biathletes ran different race formats, including the pursuit race. During this race, the biathletes chase the leader with a start time identical to the result of the sprint race previously achieved. So, pursuit involves different skills (such as tactics or management of emotional pressure) that are not present during races with an interval-start procedure like sprint. Nevertheless, final pursuit rankings are strongly correlated to sprint ones, which prevents a spectacular comeback after a disappointing sprint race. We present here an alternative pursuit ranking system that is nearly decorrelated to sprint rankings. This simple ranking system is based on comparisons with previous pursuit results. The current and the alternative rankings were then compared on different pursuit rankings, using a database of 148 results from men pursuit world cups. The alternative ranking was shown to strongly modify a single pursuit ranking but these modifications were smoothed on a whole world cup season. Advantages and limitations of the alternative ranking system are discussed, paving the way to a fairer modification of the current pursuit ranking to increase surprise and suspense in biathlon pursuit races.
Purification to Homogeneity of UDP-g 1 ucose : Glycoprotein Glucosyltransferase from Schizosaccharomyces pombe and Apparent Absence of the Enzyme from Saccharomyces cerevisiae *

The UDP-G1c:glycoprotein glucosyltransferase was purified to homogeneity from the fission yeast Schizosaccharomyces pombe. The enzyme has been recently suggested to be involved in the mechanism by which unfolded, partially folded, or misfolded glycoproteins are retained in the endoplasmic reticulum. The pure yeast glucosyltransferase formed protein-linked Glc,Maq,GlcNAc,,Glc,Man,GlcNAc,, and Glc,Man,GlcNAc, when incubated with UDP-Glc and denatured thyroglobulin. The same compounds were formed upon glucosylation of endogenous acceptors by crude microsomes. The enzyme was a soluble microsomal protein that required Ca2+ for activity, used UDP-Glc and not TDP-Glc, ADP-Glc, or UDP-Gal as sugar donor, had an almost neutral optimum pH value, and as the glucosyltransferase obtained from rat liver, glucosylated denatured but not native glycoproteins or glycopeptides. A similar enzymatic activity could not be detected in Saccharomyces cerevisiae microsomes and transient glucosylation of glycoproteins (addition of a single glucose unit to glucose-free oligosaccharides by the glucosyltransferase followed by its removal by glucosidase 11) could not be detected in intact S. cerevisiae cells. These are the only eukaryotic cells described so far in which these processing reactions of the endoplasmic reticulum do not occur. Availability of the pure S. pombe enzyme will eventually allow testing the possible involvement of the glucosyltransferase in sensing glycoprotein tertiary structures in the endoplasmic reticulum.
The Impact of Artificial Intelligence on Contemporary Orthodontic Treatment Planning - A Systematic Review and Meta-Analysis

Objective: To assess available evidence on the use of artificial intelligence (AI) in the planning of customized orthodontic therapy. The aim of the meta-analysis was to evaluate the performance and effectiveness of AI models for orthodontic treatment planning and decision-making. Materials and methods: PubMed, EBSCO host, ScienceDirect, Scopus, and Web of Science were searched over the period from January 1, 2000 to January 9, 2021, then they were updated until January 19, 2022. A systematic review and diagnostic test accuracy meta-analysis were performed. Results: Overall, 1037 records were identified. A total of twelve studies were ultimately included in the qualitative synthesis, of which five studies were included in the meta-analysis. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve with 95% confidence intervals of AI models’ performance were: 0.965 (0.921-0.985), 0.962 (0878-0.989), 695.537 (232.742-2078.572), 0.99 (0.98-1.00), respectively. The accuracy of AI systems reached 95.47%. Conclusions: The findings show promising results concerning the diagnostic accuracy of AI systems for orthodontic treatment planning and decision-making and its implementation in clinical settings. AI models are successful in predicting valid treatment plans with accurate decisions. Thus, they can ease global treatment and improve outcomes.
A Taxonomy of Visualization Research Cases of Sentiment Analysis Data based on Visual Metaphors

This study proposes a taxonomy to analyse visualization cases of sentiment data based on visual metaphors. In order to create a taxonomy, we established a group of classiﬁcation criteria including sentiment analysis, metaphor analysis, visualization technique as well as analysis objective and classiﬁed the papers according to the criteria. This study identiﬁes the effects of conducting sentiment visualization in a way that is accessible to the general public, and is expected to provide guidelines for understanding sentiment information using visual metaphors.
SpinalFuzz: Coverage-Guided Fuzzing for SpinalHDL Designs

Boosting hardware design productivity is a major plus of SpinalHDL, a Scala-based Hardware Description Language (HDL). SpinalHDL achieves this by providing object oriented programming, functional programming, and meta-hardware description finally enabling the generation of Verilog code. Despite all the advantages of SpinalHDL, verification is the biggest challenge here as well.In this paper, we bring Coverage-Guided Fuzzing (CGF), a well-established software testing technique, to the SpinalHDL design flow. We have implemented our approach SpinalFuzz on top of the fuzzer AFL++. We leverage Scala-features to automate as many tasks as possible and ease the integration of fuzzing in SpinalHDL. In the experiments we demonstrate the effectiveness of SpinalFuzz in comparison to Constrained Random Verification (CRV). For a wide range of SpinalHDL designs we show that SpinalFuzz outperforms CRV and reaches coverage-closure.
Concording U.S. Harmonized System Codes over Time

Monitoring changes to product classi cation systems is an important component of a wide range of empirical research. In this paper we develop an alogorithm for concording periodic revisions to the ten-digit Harmonized System (HS) codes used by U.S. statistical agencies to categorize international trade since 1989. We use this algorithm to construct the rst comprehensive concordance of HS codes over time, and show how this concordance can be extended to incorporate future revisions. We then characterize the extent of HS-code changes since 1989 and discuss how controlling for these revisions is critical for understanding the growth of U.S. trade. JEL classi cation: F1 Schott thanks the National Science Foundation (grants SES-0241474 and SES-0550190) and the Yale School of Management for research support. We thank Julie Linden of the Yale University Social Sciences Library for generous help in securing the U.S. trade data. We thank Mayumi Hairston Escalante for advice and for generous provision of unpublished Census obsolete-new les. The research in this paper was undertaken while Pierce was at Georgetown University. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau or the National Science Foundation. y4600 Silver Hill Road, Washington, DC 20233, email: justin.r. pierce@census.gov; telephone: 301-763-6469; fax: 301-763-5935. z135 Prospect Street, New Haven, CT 06520, tel: (203) 436-4260, fax: (203) 432-6974, email: peter.schott@yale.edu. A Concordance of U.S. HS Codes Over Time 2
State-of-the-art Machine Learning Classifiers: A Comparative Study on health data

Nowadays data mining has gained much eminence. It is a great source of information retrieval. Departments have vast collection of versatile data. It can be in the form of text, digits or images available in online data repositories and data ware houses. In medical field data is related to disease diagnosis, patient’s recommendation and different medical situations. Machine learning technique is very helpful in data mining. Machine learning techniques help to analyze Health datasets that can provide facilitation in disease diagnosis and decision making. Previous comparative research work on the machine learning techniques is mostly limited to a single dataset evaluation with few parameters and mostly deficit of statistic performance metrics graphical presentation. The purpose of this research work is to propose and conduct an empirical analysis of multiple widely used machine learning classifiers on health datasets through accuracy, sensitivity, specificity and time parameters measurements. Naïve Bayes, LMT, SMO, JRip and J48 Algorithm are used to empirically analyze the Kidney, Diabetes and Liver datasets. The empirical analysis of five classifiers with numeric metrics and performance graphics results that J48 classifier gave best performance on three datasets with different cross validation ratios as compared to other algorithms.
Knowledge Graph Attention for Sequential Recommendations

Sequential recommendation systems model dynamic preferences of users based on their historical interactions with platforms. Despite recent progress, modeling short-term and long-term behavior of users in such systems is nontrivial and challenging. To address this, we present a solution enhanced by a knowledge graph called KATRec (Knowledge Aware aTtentive sequential Recommendations). KATRec learns the short and long-term interests of users by modeling their sequence of interacted items and leveraging pre-existing side information through a knowledge graph attention network. Our novel knowledge graph-enhanced sequential recommender contains item multi-relations at the entity-level and users’ dynamic sequences at the item-level. KATRec improves item representation learning by considering higher-order connections and incorporating them in user preference representation while recommending the next item. Experiments on three public datasets show that KATRec outperforms state-of-the-art recommendation models and demonstrates the importance of modeling both temporal and side information to achieve high-quality recommendations.