Miracle-dz/UniFAR-FTUs huggingface dataset:
The Algorithm of Factor 12: Generating the Information Carved on the Plimpton 322 Tablet

The right triangles represented in the Plimpton 322 data have integer sides $(a, b, d)$ with $a < b < d$, by inspecting the data we propose $b=M\,Q_{M}$ with $M,Q_{M}$ positive integers. In this way we present a new data-driven family of algorithms in order to generate the whole content of the Plimpton-322 tablet. Each algorithm of the family corresponds to one given value of the bundling factor or mak\c{s}arum $M$. The algorithm with $M=12$ arise naturally from data and is developed entirely through this work. We could generate the complete Plimpton 322 table by using three different schemes in the framework of the twelve factor algorithm. Finally we found by varying $M$ in the general algorithm, we can be able to find all the Pythagorean Triples.
Generation and sustenance of electric fields in sandstorms

Sandstorms are frequently accompanied by the generation of intense electric fields and lightning. In a very narrow region close to the ground level, sand particles undergo a charge exchange mechanism whereby larger (resp. smaller) sized sand grains become positively (resp. negatively) charged are then entrained by the turbulent fluid motion. Our central hypothesis is that differently sized sand particles get differentially transported by the turbulent flow resulting in a large-scale charge separation, and hence a large-scale electric field. We utilize our simulation framework, comprising of large-eddy simulation of the turbulent atmospheric boundary layer along with sand particle transport and an electrostatic Poisson solver, to investigate the physics of electric fields in sandstorms and thus, to confirm our hypothesis. We utilize the simulation framework to investigate electric fields in weak to strong sandstorms that are characterized by the number density of the sand particles. Our simulations reproduce observational measurements of both mean and RMS fluctuation values of the electric field. We propose a scaling law in which the electric field scales as the two-thirds power of the number density that holds for weak-to-medium sandstorms.
Scalable Psychological Momentum Forecasting in Esports

The world of competitive Esports and video gaming has seen and continues to experience steady growth in popularity and complexity. Correspondingly, more research on the topic is being published, ranging from social network analyses to the benchmarking of advanced artificial intelligence systems in playing against humans. In this paper, we present ongoing work on an intelligent agent recommendation engine that suggests actions to players in order to maximise success and enjoyment, both in the space of in-game choices, as well as decisions made around play session timing in the broader context. By leveraging temporal data and appropriate models, we show that a learned representation of player psychological momentum, and of tilt, can be used, in combination with player expertise, to achieve state-of-the-art performance in pre- and post-draft win prediction. Our progress toward fulfilling the potential for deriving optimal recommendations is documented.
A Type-2 Fuzzy Logic Based System for Decision Support to Minimize Financial Default in the Sudanese Banking Sector

The recent global financial-economic crisis has led to the collapse of several companies from all over the world. This has created the need for powerful frameworks which can predict and reduce the potential risks in financial applications. Such frameworks help organizations to enhance their services quality and productivity as well as reducing the financial risk. The widely used techniques to build predictive models in the financial sector are based on statistical regression, which is deployed in many financial applications such as risk forecasting, customers’ loan default and fraud detection. However, in the last few years, the use of Artificial Intelligence (AI) techniques has increased in many financial institutions because they can provide powerful predictive models. However, the vast majority of the existing AI techniques employ black box models like Support Vector Machine (SVMs) and Neural Network (NNs) which are not able to give clear and transparent reasoning to explain the extracted decision. However, nowadays transparent reasoning models are highly needed for financial applications. This paper presents a type-2 fuzzy logic system for predicting default in financial systems. the researchers used a real dataset collected from the banking sector in Sudan. The proposed system resulted in transparent outputs which could be easily understood, analyzed and augmented by the human stakeholders. Besides, the proposed system resulted in an average recall of 83.5%, which outperformed its type-1 counterpart by 20.66%.
Geostatistical Modeling and Prediction Using Mixed Precision Tile Cholesky Factorization

Geostatistics represents one of the most challenging classes of scientific applications due to the desire to incorporate an ever increasing number of geospatial locations to accurately model and predict environmental phenomena. For example, the evaluation of the Gaussian log-likelihood function, which constitutes the main computational phase, involves solving systems of linear equations with a large dense symmetric and positive definite covariance matrix. Cholesky, the standard algorithm, requires O(n^3) floating point operators and has an O(n^2) memory footprint, where n is the number of geographical locations. Here, we present a mixed-precision tile algorithm to accelerate the Cholesky factorization during the log-likelihood function evaluation. Under an appropriate ordering, it operates with double-precision arithmetic on tiles around the diagonal, while reducing to single-precision arithmetic for tiles sufficiently far off. This translates into an improvement of the performance without any deterioration of the numerical accuracy of the application. We rely on the StarPU dynamic runtime system to schedule the tasks and to overlap them with data movement. To assess the performance and the accuracy of the proposed mixed-precision algorithm, we use synthetic and real datasets on various shared and distributed-memory systems possibly equipped with hardware accelerators. We compare our mixed-precision Cholesky factorization against the double-precision reference implementation as well as an independent block approximation method. We obtain an average of 1.6X performance speedup on massively parallel architectures while maintaining the accuracy necessary for modeling and prediction.
Flavonoids content of 70% methanolic extract of Erucaria pinnata (Viv.) and its effect as anticancer and antiangiognic: in vitro, in vivo and docking study

Erucaria pinnata (Viv.) is a wild annual plant growing in North-Western Coastal Region in Egypt. This study reports for the first time the cytotoxic activity of different extracts of Erucaria pinnata plant against HEP-G2 cell line. The 70% methanolic extract (E1) recorded the best potent cytotoxic activity (IC50=13.6 µg/ml), so we analysis the flavonoids constituent of this extract using HPLC, which show that our extract is rich with important flavonoids compounds (rutin, quercetin, leutolin, etc.). We evaluated its antitumor activity against hepatocellular carcinoma (HCC) induced by diethylnitrosamine (DEN) (200mg/Kg. b.wt., i.p, single dose) after two weeks, animals received carbontetrachloride (CCl4) (3ml/Kg. b.wt., SC, once a week for 6 weeks) and the experiment continued for 44 weeks in rats. After the experimental period, the administration of DEN/CCl4 showed significant increase in the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total bilirubin, gamma-glutamyl transferase (γGT) and significant decrease in the levels of total proteins and albumin content in the serum with reduction in the liver antioxidants, including superoxide dismutase (SOD) and catalase (CAT). This was accompanied by increases in serum specific tumor markers (AFP). The 70% methanolic plant extract (E1) was orally administrated (400mg/kg/day respectively) for the whole study period, and it showed a significant improvement at the different biological liver functions, remodeled the antioxidant enzymes activity and down-regulated the serum AFP. All these findings were confirmed by histopathological studies of the liver samples obtained from all groups. In addition, we evaluated its antiangeogenic activity by docking study against VEGFR-2 tyrosine kinase after it showed an ability to inhibit the VEGFR-2 expression in vitro and inhibit the concentration of VEGF-A in vivo. The hepatoprotective effect of our extract was attributed to its antioxidant and antiangeogenic activity.
An Analysis of Artificial Neural Network Techniques for Classification in Web Usage Mining

Web usage mining is a particular part of web mining that is depends on the discovery also investigation of web usage patterns from web logs so as to successfully as well as powerfully provide the requests of the users seeing the websites. The major focal point of this paper is on doing customers classification on the source of exposed patterns from web logs. The objective of this paper is to scrutinize classification techniques that utilize association rules; sequential patterns etc., from web usage mining research community related papers. User interaction with web sites generates a large amount of web access data stored in the web access logs. Those data can be used for ecommerce to conduct an evaluation of possessed website pages as one of the efforts to understand the desires of the user. Through classification techniques in web usage mining, there are many models in literature and practice that analyze user behavior based on user navigation data and use classification algorithms to characterize their access patterns. The navigation patterns identified are expected to capture the user's interests.
Compact Area and Performance Modelling for Coarse-Grained Reconfigurable Architectures

Compact Area and Performance Modelling for Coarse-Grained Reconfigurable Architectures Kuang-Ping Niu Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2019 We consider area and performance modelling for coarse-grained reconfigurable architectures (CGRAs) and extend the open-source CGRA-ME (CGRA modelling and exploration) framework to rapidly estimate these metrics. Area is modelled by synthesizing commonly occurring CGRA primitives in isolation, and then aggregating the primitives’ component-wise areas. Performance is modelled by integrating a static-timing analysis (STA) framework into CGRA-ME. The delays in the STA timing graph are based on component-wise delays, as well as estimated interconnect delay. Experimental results using the estimation engine demonstrate reasonably accurate estimation for both area and performance for different CGRA architectures, as well as different variations of the same architecture. By offering fast and accurate estimation in an early phase of CGRA architecture exploration, the estimation engine allows the user to bypass the lengthy process of a full VLSI implementation, and rapidly explore the area/performance architecture space.
Proxy Variables of Online Engagement on the Learning Management System

The wide-spread adoption of Learning Management System (LMS) technology has fundamentally changed the environment for today’s teaching and learning. The LMS has generated a new set of data that could possibly serve as proxy measures of learners’ engagement online. LMS data sets can be mined and analyzed to provide meaningful measures of learners’ online engagement to support and enhance teaching and learning practices. In particular, this research focuses on the digital traces of LMS data to establish and validate meaningful proxies of online engagement. With the vast data sets available over a LMS, it is timely to identify a methodical approach to extract and transform data features in a modus that is useful for analysis. Hence, the purpose of this study is twofold: (1) to establish a common set of metrics (immediacy, frequency, interval and recency) to codify and quantify learners’ online engagement in the LMS; and (2) to validate, using data mining techniques, these metrics of online engagement patterns in relation to the learners’ academic performance.
Morphological and Genetic Variability of the Mass Whitefish Forms in Lake Onega

In Lake Onega, the whitefish Coregonus lavaretus has been shown to occur as a variety of forms. Medium- and sparsely-ranked whitefish are most abundant. Analysis of available data indicates that whitefish populations from Karelia’s large lakes display the maximum values of various genetic variability indices. This fact seems to be due to the history of the colonization of the lake by the discrete evolutionary whitefish lineages from various Late Quaternary habitats followed by their hybridization. A great variety of Onega whitefish haplotypes is probably related to the genetic heterogeneity of the whitefish who until recently had occurred as five ecological forms ranking as subspecies. The median network obtained suggests that many of the populations studied have become less abundant. The well-defined “star-like” network structure is characteristic of populations that passed through a narrow “bottleneck” in the near past and then expanded rapidly, as indicated by the abundance of rare haplotype varieties. It seems that the retreat of the Scandinavian glacier was not a momentary event but took a long time during which the populations formed were subjected to demographic transformations.
Research on defect inspection method of pipeline robot based on adaptive image enhancement

In view of the problem about uneven image acquisition and inaccurate edge extraction in pipeline detection process, a pipeline robot defect inspection method based on adaptive image enhancement is proposed. Firstly, a single-scale Retinex adaptive image enhancement algorithm is designed, which uses the guided filter to estimate the illumination component of the Value component of the image, and gets the illumination equilibrium image by adaptive Gamma correction, so as to realize the image enhancement. Then, the traditional Canny edge detection method is improved, using bilateral filtering to smooth the image. Besides, the defect images are segmented by the iterative threshold method, and the edge connection is carried out according to the edge pixel similarity. Therefore, the defect contour of the pipe-wall is extracted effectively. Thirdly, a pipeline robot defect detection system based on adaptive image enhancement is built, and a crawler car equipped with the pan-tilt-zoom camera conducts all-round visual inspection of the defects in the pipeline inner wall. The experimental results show that the detection method in this paper can adaptively correct the image brightness, and the uneven brightness of the image is significantly improved. Compared with the sub-optimal algorithm, the information entropy of the image is increased by 2.4%, the average gradient of the image is increased by 2.3%, and the peak signal to noise ratio is increased by 4.4%, and the pipeline defect edges are extracted effectively with the detection accuracy up to 97%.
Attenuation of reserpine-induced fibromyalgia via ROS and serotonergic pathway modulation by fisetin, a plant flavonoid polyphenol

Fibromyalgia (FM) is a chronic complex musculoskeletal disorder characterized by widespread musculoskeletal pain accompanied by fatigue, sleep disturbance, memory defects and mood changes. Fisetin, a plant flavonoid polyphenol, has been reported to possess potent antioxidant, antinociceptive and neuroprotective activities. The present study aimed to evaluate the efficacy of fisetin against reserpine-induced FM (RIF) in rats. RIF was induced in male Wistar rats (180–220 gm) using reserpine (1 mg/kg; subcutaneous; once daily for 3 consecutive days) and the rats were treated with fisetin (5, 10 and 25 mg/kg) for 21 days. Various behavioral, biochemical and molecular parameters were evaluated. Administration of reserpine induced allodynia, hyperalgesia and depression, which were significantly ameliorated (P<0.05) by fisetin (10 and 25 mg/kg), as reflected by an increase in paw and tail withdrawal latency, increased paw withdrawal threshold, and decreased immobility time. Reserpine led to decreased biogenic amine levels [5-hydroxytryptamine (5-HT), noradrenaline (NA) and dopamine (DA)] and increased the ratio to their metabolite 3,4-dihydroxyphenylacetic acid. 5-hydroxyindoleacetic acid in the spinal cord, thalamus and prefrontal cortex was significantly decreased (P<0.05) by fisetin. Immunohistological analysis of brain tissue revealed that fisetin significantly inhibited (P<0.05) reserpine-induced depletion of 5-HT. It also significantly inhibited (P<0.05) elevated oxido-nitrosative stress and reactive oxygen species (ROS) levels, as analyzed by flow cytometry in RIF rats. Fisetin exerts its antinociceptive and anti-depressive potential via modulation of decreased levels of biogenic amines (5-HT, NA and DA), elevated oxido-nitrosative stress and ROS to ameliorate allodynia, hyperalgesia, and depression in experimental RIF.
Organizational culture types and individual readiness for change: evidence from Indonesia

This study aims to examine the effect of organizational culture types on individual readiness for change in Indonesia as a country with high collectivism. Specifically, this study examines the effect of four types of culture namely clan, adhocracy, market, and hierarchy on individual readiness for change. The sample of this study was 264 employees of XYZ Company, a family company in Indonesia that made changes by releasing part of its shares into the stock market. Test results using simple regression support the hypothesis that clan culture and adhocracy have a positive effect on individual readiness for change. However, the influence of market culture and hierarchy on individual readiness for change was also found to be positive instead of negative as hypothesized. Discussions and suggestions for future research are presented.
University of Southern Denmark Adaptive control strategies for interlimb coordination in legged robots

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Stimulating release of undersized fish through a square mesh panel in the Basque otter 1 trawl fishery 2

a AZTI-Tecnalia, Marine Research Division, Txatxarramendi ugartea w/n, 48395 Sukarrieta, Spain 4 b SINTEF Ocean, Fishing Gear Technology, Willemoesvej 2, 9850, Hirtshals, Denmark 5 c The Arctic University of Norway, UiT, Breivika, N-9037 Tromsø, Norway 6 d SINTEF Ocean, Fishing Gear Technology, Brattørkaia 17C, 7010 Trondheim, Norway 7 e Institute of Marine Research, Postboks 1870 Nordnes, Bergen, 5817, Norway 8 9 10 * Corresponding author at: AZTI-Tecnalia, Marine Research Division, Txatxarramendi ugartea w/n, 48395 Sukarrieta, 11 Spain 12 E-mail address: elsacuende@gmail.com (E. Cuende) 13 14 15 16 Abstract 17
Improvement of the whole lip contour by additional filler injection to the lateral vermilion zone: a report of three cases

Hyaluronic acid (HA) fillers have been widely used to change the lip contour. Most techniques were focused on volume augmentation and wrinkle reduction. In young women, fillers are used for lip volume enhancement, while in older women, fillers are used for both lip volume and vermilion border enhancement. Until now, the most reliable method to improve the lateral contour of the upper lip has not been reported. This study aimed to improve the ptotic contour of the lateral lip by filler injection. HA filler was injected into the vermilion medial zone in the lower lip (approximately 0.2 ml on each side) through the vermilion border and central vermilion through the dry and wet junction in the upper lip (0.05-0.1 ml) for volume augmentation. Then, the filler was injected into the lateral vermilion zone between the flat and rolling-in parts in the upper lip through the upper vermilion border to improve ptotic appearance (0.1 ml on each side). Three patients underwent the procedure using the abovementioned method. The whole contour of the upper vermilion border was significantly changed from flat to round and curved. Even though the position of the mouth corner was not changed, it appeared lifted, which was caused by the illusion of eversion and anterior projection of the medial vermilion and changed contour of the lateral vermilion in the upper lip. The three patients were satisfied with their dynamic and vibrant-looking lips. Additional filler injection to the lateral vermilion zone to improve the whole contour of the lips was considered a relatively easy technique for three young patients. Level of Evidence: IV
BIOSAFETY MEASURES ADOPTED BY NURSING UNDERGRADUATES IN DAILY ACTIVITIES IN SPACES OF LABORATORY PRACTICES

Objective: to analyze the biosafety measures adopted by nursing undergraduates in daily activities in spaces of laboratory practices.Method: descriptive, qualitative analysis study performed at a Higher Education Institution in the period from April to June 2018, with 30 students. For the analyses of interviews, Collective Subject Discourse was utilized. Results: as for Personal Protective Equipment (PPE) usage, 24% used gloves, followed by white coat and mask, with 19% and 18% each, respectively. Referring to moments of Hand Hygiene (HH) during the laboratory practices, 32% performed the hygiene before and 26% after the practice. It is observed that the scholars’ knowledge about biosafety is broad but limited to PPE usage. Conclusion: it is verified that the use of biosafety measures in this environment is still a challenge, for it be limited to PPE and HH usage.
Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement

Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. This study compares the performance of the Naive Bayes method and C4.5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination. As part of this study we also compare the performance of the classification model from a number of scenarios involving a combination of preprocessing methods, namely Synthetic Minority Over-Sampling Technique (SMOTE) and Wrapper feature selection method, with both classification techniques. The scenario of C4.5 method combined with SMOTE and feature selection method produces the best performance in classifying readmissions of diabetic patients with an accuracy value of 82.74 %, precision value of 87.1 %, and recall value of 82.7 %.