Miracle-dz/UniFAR-FTUs huggingface dataset:
DL-GuesS: Deep Learning and Sentiment Analysis-based Cryptocurrency Price Prediction

Cryptocurrencies are peer-to-peer-based transaction systems where the data exchanges are secured using the secure hash algorithm (SHA)-256 and message digest (MD)-5 algorithms. The prices of cryptocurrencies are highly volatile and follow stochastic moments and have reached their unpredictable limits. They are commonly used for investment and have become a substitute for other types of investment like metals, estates, and the stock market. Their importance in the market raises the strict requirement for a sturdy forecasting model. However, cryptocurrency price prediction is quite challenging due to its dependency on other cryptocurrencies. Many researchers have used machine learning and deep learning models, and other market sentiment-based models to predict the price of cryptocurrencies. As all the cryptocurrencies belong to a specific class, we can infer that the increase in the price of one cryptocurrency can lead to a price change for other cryptocurrencies. Researchers had also utilized the sentiments from tweets and other social media platforms to increase the performance of their proposed system. Motivated by these, in this paper, we propose a hybrid and robust framework, DL-Gues, for cryptocurrency price prediction, that considers its interdependency on other cryptocurrencies and also on market sentiments. We have considered price prediction of Dash carried out using price history and tweets of Dash, Litecoin, and Bitcoin for various loss functions for validation. Further, to check the usability of DL-GuesS on other cryptocurrencies, we have also inferred results for price prediction of Bitcoin-Cash with the price history and tweets of Bitcoin-Cash, Litecoin, and Bitcoin.
Classification of KPI lumps

A large family of nonsingular rational solutions of the Kadomtsev–Petviashvili (KP) I equation are investigated. These solutions are constructed via the Gramian method and are identified as points in a complex Grassmannian. Each solution is a traveling wave moving with a uniform background velocity but have multiple peaks which evolve at a slower time scale in the co-moving frame. For large times, these peaks separate and form well-defined wave patterns in the xy-plane. The pattern formation are described by the roots of well-known polynomials arising in the study of rational solutions of Painlevé II and IV equations. This family of solutions are shown to be described by the classical Schur functions associated with partitions of integers and irreducible representations of the symmetric group of N objects. It is then shown that there exists a one-to-one correspondence between the KPI rational solutions considered in this article and partitions of a positive integer N.
Anomalies Prediction in Radon Time Series for Earthquake Likelihood Using Machine Learning-Based Ensemble Model

The ability to predict the radioactive soil radon gas concentration is important for human beings because it serves as a precursor to earthquakes. Several studies have been conducted across the globe to confirm the correlation of radon emission dynamics and earthquakes, and concluded that the soil radon gas is the witness of anomalous behaviour before the occurrences of several earthquakes. This anomalous behavior can help to construct a better prediction model for earthquake forecasting. This paper aims at employing different ensemble and individual machine learning methods on real time radon time series data with different scenarios to predict anomalies in data caused by the seismic activities.The ensemble methods include boosted tree, bagged cart and boosted linear model while standalone machine learning methods include support vector machine with linear and radial kernels and k-nearest neighbors ( ${K}$ -NN). We tested the methods on a dataset recorded on the fault line located in Muzaffarabad. Time series data was collected over a period ranging from March 1, 2017 to May 11, 2018 including nine(09) earthquakes. The methods are tested in four different settings with 10 times 10 folds cross validation procedure over the time window of 1 to 4. The repeated 10 fold cross validation is performed to reduce the noise in the model performance estimation by replicating the 10 fold cross validation procedure 10 times. Statistical performance evaluation measures viz. root mean square error (RMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), percentage bias (PB), and mean squared error (MSE) have been calculated for the assessment of performance. In setting 1, the support vector machine with radial kernel performs better with the minimum RMSE score of 1381.023 when compared to other prediction models. In setting 3, it can be observed through different performance metrics such as RMSE, the value in the range [1262.864, 1409.616] which is minimum when other prediction models for predicting soil radon gas concentration dataset. For setting 4, the boosted tree model yielded the minimum RMSE and MAPE scores of 1573.174 and 0.056 respectively. Findings of the study shows that boosted tree and support vector machine with radial kernel proved to be better regression models for the prediction of anomalies in soil radon gas concentration during seismic activities. An important finding of this study suggests that by employing boosted tree ensemble method make us able to accurately predict soil radon gas concentration automatically from environmental parameters.
An Investigation of Chemo‐Mechanical Phenomena and Li Metal Penetration in All‐Solid‐State Lithium Metal Batteries Using In Situ Optical Curvature Measurements

Solid‐electrolytes (SEs) can provide a pathway to increase energy‐density in lithium metal batteries. However, lithium metal penetration through garnet based LLZO solid electrolytes has been identified as a critical failure process. This phenomenon is related to chemo‐mechanical processes which are difficult to probe. In particular, characterizing the dynamic mechanical deformations that occur in electrode‐SE structures is very challenging. This study reports in situ curvature measurements that are thus designed to probe chemo‐mechanical phenomena that occur during lithium plating. The novel experimental cell configuration created for this work shows that pressure builds up in the Li metal during plating, up until the point where short circuits occur. The resulting data are analyzed with a detailed finite element model (FEM) to quantitatively evaluate stress evolution. The results show that Li metal plating within a surface flaw can produce stress build‐up prior to short‐circuiting. The combined results from both the experiments and the FEM suggest that it is critical to minimize surface defects and flaws during the manufacturing processes.
Human Automotive Interaction: Affect Recognition for Motor Trend Magazine's Best Driver Car of the Year

Observation analysis of vehicle operators has the potential to address the growing trend of motor vehicle accidents. Methods are needed to automatically detect heavy cognitive load and distraction to warn drivers in poor psychophysiological state. Existing methods to monitor a driver have included prediction from steering behavior, smart phone warn‐ ing systems, gaze detection, and electroencephalogram. We build upon these approaches by detecting cues that indicate inattention and stress from video . The system is tested and developed on data from Motor Trend Magazine's Best Driver Car of the Year 2014 and 2015. It was found that face detection and facial feature encoding posed the most dif‐ ficult challenges to automatic facial emotion recognition in practice. The chapter focuses on two important parts of the facial emotion recognition pipeline: (1) face detection and (2) facial appearance features. We propose a face detector that unifies state‐of‐the‐art approaches and provides quality control for face detection results, called reference‐based face detection. We also propose a novel method for facial feature extraction that com‐ pactly encodes the spatiotemporal behavior of the face and removes background texture, called local anisotropic‐inhibited binary patterns in three orthogonal planes. Real‐world results show promise for the automatic observation of driver inattention and stress.
Effects of Abelmoschus manihot Flower Extract on Enhancing Sexual Arousal and Reproductive Performance in Zebrafish

The flower of Abelmoschus manihot L. is mainly used for the treatment of chronic kidney diseases, and has been reported to have bioactivities such as antioxidant, anti-inflammatory, antiviral, and antidepressant activities. This study used wild-type adult zebrafish as an animal model to elucidate the potential bioactivity of A. manihot flower ethanol extract (AME) in enhancing their sexual and reproductive functions. Zebrafish were fed AME twice a day at doses of 0.2%, 1%, and 10% for 28 days, and were then given the normal feed for an additional 14 days. The hormone 17-β estradiol was used as the positive control. Sexual behavioral parameters such as the number of times males chased female fish, the production of fertilized eggs, and the hatching rate of the fertilized eggs were recorded at days 0.33, 7, 14, 21, 28, and 42. The expression levels of sex-related genes—including lhcgr, ar, cyp19a1a, and cyp19a1b—were also examined. The results showed that the chasing number, fertilized egg production, and hatching rate were all increased with the increase in the AME treatment dose and treatment time. After feeding with 1% and 10% AME for 28 days, the chasing number in the treated group as compared to the control group increased by 1.52 times and 1.64 times, respectively; the yield of fertilized eggs increased by 1.59 times and 2.31 times, respectively; and the hatching rate increased by 1.26 times and 1.69 times, respectively. All three parameters exhibited strong linear correlations with one another (p < 0.001). The expression of all four genes was also upregulated with increasing AME dose and treatment duration. When feeding with 0.2%, 1%, and 10% AME for 28 days, the four sex-related genes were upregulated at ranges of 1.79–2.08-fold, 2.74–3.73-fold, and 3.30–4.66-fold, respectively. Furthermore, the effect of AME was persistent, as the promotion effect continued after the treatment was stopped for at least two weeks. The present findings suggest that AME can enhance the endocrine system and may improve libido and reproductive performance in zebrafish.
UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering

BACKGROUND Computational biomedical simulations frequently contain parameters that model physical features, material coefficients, and physiological effects, whose values are typically assumed known a priori. Understanding the effect of variability in those assumed values is currently a topic of great interest. A general-purpose software tool that quantifies how variation in these parameters affects model outputs is not broadly available in biomedicine. For this reason, we developed the 'UncertainSCI' uncertainty quantification software suite to facilitate analysis of uncertainty due to parametric variability. METHODS We developed and distributed a new open-source Python-based software tool, UncertainSCI, which employs advanced parameter sampling techniques to build polynomial chaos (PC) emulators that can be used to predict model outputs for general parameter values. Uncertainty of model outputs is studied by modeling parameters as random variables, and model output statistics and sensitivities are then easily computed from the emulator. Our approaches utilize modern, near-optimal techniques for sampling and PC construction based on weighted Fekete points constructed by subsampling from a suitably randomized candidate set. RESULTS Concentrating on two test cases-modeling bioelectric potentials in the heart and electric stimulation in the brain-we illustrate the use of UncertainSCI to estimate variability, statistics, and sensitivities associated with multiple parameters in these models. CONCLUSION UncertainSCI is a powerful yet lightweight tool enabling sophisticated probing of parametric variability and uncertainty in biomedical simulations. Its non-intrusive pipeline allows users to leverage existing software libraries and suites to accurately ascertain parametric uncertainty in a variety of applications.
The grade 9 maths ANA - what can we see after three years?

INTRODUCTION The Annual National Assessments (ANA) were introduced to address South Africa's poor performance in international mathematics assessments such as TIMSS and SACMEQ. The Grade 9 test was first written in 2012 with a national average of 12.7%. In 2013 the average increased to 13.9% but then dropped to 10.8% in 2014. The overall picture is one of very poor performance with the result that many questions are being asked about the value and purpose of the ANA. The Department of Basic Education (DBE) identifies three purposes for the ANAs : (1) a measure for the state to gauge improvement in the education system, based on learner performance year-on-year; (2) a diagnostic assessment to identify areas of weakness in learners' performance; and (3) to provide a model of good assessment practice. While these are all important goals, they are not necessarily achievable through a single instrument. However, this is not the focus of our article. Our purpose here is to report on a content analysis of the three ANA papers which we undertook in the first quarter of 2015, paying particular attention to cognitive demand and levels of difficulty. We share our findings on these aspects, as well as on trends that arise as we look across the first three years of the Grade 9 Maths ANA.
Blockchain-Based Smart Contracts in Financial Industry

: Smart contracts are is a self-executing contract that automatically executes when pre-defined conditions are met including financial services industry. Smart contracts, which are enabled by blockchain or distributed ledgers, have been held as a solution to many of the issues associated with traditional financial contracts, which are simply not designed for the digital age. Financial intermediaries raise overhead expenses and enhance regulatory requirements while enabling interoperability for the financial system and lowering risk. This concept aims to adopt smart contracts that will lead to reduced risks, lower administration and service costs, and more efficient business processes across all major segments of the financial services industry.
To Evaluate the Immunomodulatory Activity of Hydroalcoholic and Methanolic Extract of Quisqualis indica Linn in Rats IMMUNOMODULATORY ACTIVITY: Carbon Clearance test: 22

The goal of the present study is to evaluate the immunomodulatory activity of hydroalcoholic and methanolic extract of Quisqualis Indica Linn in wistar rats. Quisqualis indica (also known as Rangoon Creeper, Madhumalti, or Laal-chameli) hydroalcoholic and methanolic extracts were given orally at dosage of 100 mg/kg and 200 mg/kg. Levamisol 50 mg/kg used as standard. The carbon clearance test, Cyclophosphamide-induced myelosuppression, Total Leukocyte Count (TLC), and Differential Leukocyte Count (DLC) were used to determine immunomodulatory activity on specific and non-specific immunity (DLC). The carbon clearance test was used to determine the effect of Quisqualis indica (QI) flower extract on phagocytic activity. In carbon clearance test, QI flower extract treated all groups, exhibited significantly high phagocytic index. The phagocytic index of (100 mg/kg) and QI flower extract (200 mg/kg) showed significant (p<0.05) increased in phagocytic index when compared to control group. This indicates stimulation of the reticuloendothelial system. Cyclophosphamide at the dose of 30 mg/kg, caused a significant reduction in total WBC count in rat as compared to control group. The rise in the total WBC count lowered by Cyclophosphamide was observed
Characterization of phenotypic traits related to loss-of-function and ectopic expression of bHLH093 and bHLH061 in Arabidopsis thaliana

iv Acknowledgments . v List of Tables and Figures . ix Introduction . 1 Plant morphogenesis . 1 Arabidopsis thaliana as model system for development . 1 Genetic regulation of development; bHLH proteins . 3 bHLH proteins in Arabidopsis . 4 bHLH proteins in stomatal development . 5 bHLH061 and bHLH093 as potential regulators of stomatal development . 10 Functional roles for bHLH061 and bHLH093 from recent literature . 11 Further investigation of bHLH093 and bHLH061 . 14 Methods . 15 Plant material and growth conditions . 15 DNA extraction. 16 Primers . 16 Vector construction . 17 Directional cloning via TOPO® . 19 Ectopic overexpression vector construction . 20 Construct verification . 22 Agrobacterium tumefaciens transformation.23 Arabidopsis transformation . 23
The contribution of the Framework Programmes to major innovations

This report presents the overall findings of the Study on Contribution of the Framework Programmes to Major Innovations (N° RTD-Major Innovations-2013-A5). The aim of the project is to evaluate the contribution of the European research programmes (FP5, FP6 and FP7) to the development of a selected number of Major Innovations. The focus of the study is on the identification of the key elements explaining the factors and conditions that brought the respective Major Innovation about, and the contribution of the EU funded research. The results of the study are targeted for supporting the policy evaluation and understanding of the areas for further improvement. The study has analysed different cases and their wider context in detail. The main conclusions drawn for the analysis can be summarised as follows: 1. Research and Development, although important, is one of many drivers for major innovations and despite the fact that the FP’s have not directly contributed to breakthroughs in the Major Innovations, they have significantly contributed to the relevant innovative capacity of firms, fostering the wider innovation environment (e.g. clusters and value chains), and to related incremental innovations. 2. Major Innovations usually consist of so-called families of innovations, which together are a necessary condition to make the innovation happen. Each of the innovation families has a different “ownership”, pace and timing to be mature enough to enter the market. (The recent extension to “closer to market” activities in Horizon 2020 offers opportunities for further synchronization). 3. The exploratory and excellence driven nature of FP5 to FP7 aimed at pre-competitive research (TRL 1-4), whereas 8 out of 10 Major Innovations passed TRL9. This implies that much of the results of the FP’s may contribute to the Major Innovations through networking, aligning agendas, knowledge creation and diffusion in relevant families of innovations and to understanding the more general framework conditions for an innovation. 4. Major Innovations strongly depend on a high impact of policies and regulations outside of the specific R&D&I domain (for instance the telecom liberalization, energy policies or the GPS Dual use policy). The FPs have helped to create conditions and potential pathways to leverage such policies into Major Innovations.
Developing a high throughput PCR method to detect a broad range of X. fastidisoa strains in Vitis vinifera and Vitis labrusca

Xylella fastidiosa is a plant bacterial pathogen that colonizes the xylem of a wide range of host plants and is spread by sap-sucking insect vectors. There have been large economic losses attributed to the presence of X. fastidiosa within crops. X. fastidiosa is known as Pierce’s disease in grapevine. Pierce’s disease is a problem that affects grape crops and there has yet to be an effective way to reliably identify its presence in crops at an early stage of infection to prevent spreading and crop loss. This project developed a method to test for Pierce’s disease in wild Vitis labrusca and cultivated V. vinifera grapevine, suspected to be infected with X. fastidiosa. Previous studies provided support for using polymerase chain reaction (PCR) as a method to target and amplify genes present in the X. fastidiosa genome to identify the presence of Pierce’s disease in grapevine. Primers from previous papers, as well as selfdesigned primers, were used to test the DNA isolated from V. labrusca and V. vinifera grapevine leaves. The PCR results showed no amplification of the X. fastidiosa genes, but primer-dimers or non-target amplification were detected in some cases. The lack of amplification could suggest the absence of X. fastidiosa within the tested grapevine leaves or that the X. fastidioa DNA was present at such a low concentration, it was not amplified by the primers. Further testing is needed for developing reliable methodology for amplification of the trace amounts of X. fastidiosa DNA found within grapevines.
A Study of the Impact of Information Blackouts on the Bullwhip Effect of a Supply Chain Using Discrete-Event Simulations

This study adds to the supply chain management literature by introducing and investigating information blackouts, sudden and short-duration failure of the information flow. This study aims to contribute to the literature in following ways: first, to define information blackouts in a supply chain. Second, to investigate the response of supply chains to information blackouts using discrete-event simulation. Prior research has focused more on analyzing systemic disruptions to supply chains from well-known sources. We expect the results of this study to be useful to supply chain managers in disaster prone areas.
Indigenous Textbooks and Case Studies as Effective Tools in Business and Management Education: A Comparative Analysis of Indian versus African Business Literature

Indigenous knowledge is that home grown, sustainable form of knowledge that is capable of triggering creativity and innovation, due to the spontaneity and familiarity it is synonymous with. In teaching and learning, it is widely recognized that building an indigenous knowledge based curriculum is essential to getting the active attention and sustained interest of students. While in several parts of the world, academic literature, for the most part are products of everyday, lived realities of the students, in Africa, that is not the case. Most African textbooks are exacts replicas of European and American texts, replicated without any form of alteration, whatsoever. This article focuses on select business literature in India and Africa, to state that while India has bridged the indigenous knowledge gap in business and management education by ensuring the strong presence of indigenous knowledge in the prescribed textbooks for students, African universities still prescribe foreign, and therefore, alien textbooks to business and management students. The end result is G. R. Woodman and B. Morse’s (1987) observation that the difficulty of designing viable development strategies in Africa is based on the fact that the region’s modern development thinking is not the direct descendant of, or an adaptation of the indigenous knowledge of the continent.
Cache-Coherent Shared Locking for Transactionally Consistent Updates in Near-Data Processing DBMS on Smart Storage

Even though near-data processing (NDP) can provably reduce data transfers and increase performance, current NDP is solely utilized in read-only settings. Slow or tedious to implement synchronization and invalidation mechanisms between host and smart storage make NDP support for data-intensive update operations difficult. In this paper, we introduce a low-latency cache-coherent shared lock table for update NDP settings in disaggre-gated memory environments. It utilizes the novel CCIX inter-connect technology and is integrated in neoDBMS , a near-data processing DBMS for smart storage. Our evaluation indicates end-to-end lock latencies of ∼ 80-100ns and robust performance under contention.