Miracle-dz/UniFAR-FTUs huggingface dataset:
Fiscal Policy and Economic Growth in Nigeria: An ARDL, Bound Test and ECM Approach

The aim of this study is to examine the relationship between fiscal policy and economic growth in which past studies have not fully explored in Nigeria. Data was collected from the Central Bank of Nigeria Statistical Bulletin from 1990 to 2017 and the study employed the Autoregressive Distributed Lag (ARDL) model and Error Correction Model (ECM) to address its objective. Consequently, the major findings that originated from the work could be submitted as follows. The result of ECM term confirmed that about 39% of the total disequilibrium in the previous year would be corrected in the current year. Therefore, it will take about two (2) years for the system to adjust back to its long run equilibrium path. Meanwhile, the estimated result shows that economic growth and government revenue have a significant positive relationship in Nigeria in the short run but the relationship becomes negative in the long run. However, recurrent expenditure has a significant negative relationship with economic growth in the short run but the relationship becomes insignificant in the long run. However, inflation rate has a significant positive relationship with economic growth in both short run and long run. Due to the findings that originated in this study, this paper makes the following recommendations for the policy makers in Nigeria that if the economic growth is the target of the policy makers, manipulating fiscal policy variables such as government revenue, capital expenditure and inflation rate appropriately will increase economic growth in the short run and the long run.
Current glacier recession causes significant rockfall increase: the immediate paraglacial response of deglaciating cirque walls

Abstract. In the European Alps, almost half the glacier volume has disappeared over the past 150 years. The loss is reflected in glacier retreat and ice surface lowering even at high altitude. In steep glacial cirques, surface lowering exposes rock to atmospheric conditions probably for the very first time in several millennia. Instability of rockwalls has long been identified as one of the direct consequences of deglaciation, but so far cirque-wide quantification of rockfall at high resolution is missing. Based on terrestrial lidar, a rockfall inventory for the permafrost-affected rockwalls of two rapidly deglaciating cirques in the Central Alps of Austria (Kitzsteinhorn) is established. Over 6 years (2011–2017), 78 rockwall scans were acquired to generate data of high spatial and temporal resolution. Overall, 632 rockfalls were registered, ranging from 0.003 to 879.4 m3, mainly originating from pre-existing structural rock weaknesses. A total of 60 % of the rockfall volume detached from less than 10 vertical metres above the glacier surface, indicating enhanced rockfall activity over tens of years following deglaciation. Debuttressing seems to play a minor effect only. Rather, preconditioning is assumed to start inside the randkluft (void between cirque wall and glacier) where measured sustained freezing and ample supply of liquid water likely cause enhanced physical weathering and high quarrying stresses. Following deglaciation, pronounced thermomechanical strain is induced and an active layer penetrates into the formerly perennially frozen bedrock. These factors likely cause the observed paraglacial rockfall increase close to the glacier surface. This paper, the first of two companion pieces, presents the most extensive dataset of high-alpine rockfall to date and the first systematic documentation of a cirque-wide erosion response of glaciated rockwalls to recent climate warming.
A Statistical Uncertainty Assessment on a LOFT L2-5 LBLOCA PCT Based on ACE-RSM

When a long run time is taken to obtain the relevant output values in a complex physical model (or code), the number of statistical samples which must be evaluated through it is a critical factor in a sampling-based uncertainty analysis. Two alternative methods have been utilized to avoid the problem associated with the size of these statistical samples: one is based on Wilks’ formula and the other is based on the conventional nonlinear regression approach. While both approaches provide a useful means to draw a conclusion on the resultant uncertainty with a limited number of code runs, there are also the corresponding unique limitations. For example, a conclusion based on the Wilks’ formula is highly affected by the sampled values themselves and the conventional regression approach requires a priori estimate on functional forms of a regression model. Main objective of this paper is to introduce a complementary method (based on the ACE algorithm) to the Wilks’ formula and the conventional regression approach for uncertainty analysis and to provide an example application to the OECD BEMUSE Phase III LOFT L2-5 LBLOCA uncertainty analysis.
Understanding the effects of toxic leadership on expatriates' readiness for innovation: an Uzbekistan case

Ever-growing business challenges emphasize the necessity for organizations to develop a competent workforce to achieve more stable and inclusive growth. Therefore, this study explored the ever-growing interest in expatriate workers to support organizational competitiveness. This study focuses on Uzbekistan, as its growing competitiveness is pressurizing leader-follower dyads and organizational innovation capabilities to improve productivity, reduce costs, and become more profitable. Workplace environments can be sensitive towards leadership behaviours that can adversely affect expatriates’ readiness for innovation. Therefore, this research study addressed the gap in empirical evidence within the leadership literature relevant to the interplay between toxic leadership and expatriates’ readiness for innovation. This qualitative descriptive study employed an explorative phenomenological cross-sectional design (n=10) into expatriates’ real-life experiences to understand the effects of toxic leadership on their readiness for innovation. The findings from the phenomenological study suggest that toxic leadership can adversely affect expatriate’s readiness for innovation.
Knowledge and perceptions of North-West University rugby players on timing of protein ingestion

The abstract should be a maximum of 250 words and be written in one continuous paragraph without subheadings. Abstracts should showcase the new information presented in the paper, either in the form of original research data or as a novel insight into an established issue. Abstracts reporting original research must include sufficient data to support any conclusion reached. It is not satisfactory to simply describe what was found (such as, "the treatment group improved more than the control group") nor to say simply that “the results will be discussed”. References should not be included.
A comparative study of state-of-the-art speech recognition models for English and Dutch

The advent of deep learning methods has led to significant improvements in speech recognition. As a result, companies are concentrating more on taking advantage of these achievements and try to utilize speech recognition in their business lines. However, data preprocessing, size of the dataset and the architecture of the deep learning model may have a huge impact on the accuracy of speech recognition and the best combination of these arrangements is still unknown in different contexts. Therefore, in this study, we aimed to figure out whether it is possible and beneficial for companies to put an effort and use these methods on relatively small datasets and in a language other than English (i.e. Dutch). In order to find the answer, we present a comparative study on two state-of-the-art speech recognition architectures on small datasets to examine the practicality and scalability of these academically well-received architectures in real-life with all its limitations and. We conducted a series of experiments to train different network architectures on different datasets. We realized that with the same data preprocessing and without using any language model, the listen, attend and spell (LAS) model on both English and Dutch datasets outperforms the CNN-BLSTM model. Comparing the gained results in this research to the previously reported results of the LAS model, it can be deduced that the size of the dataset is influential on the accuracy of speech recognition systems
Colonizer Identity and Trade in Africa: Were the British More Favourable to Free Trade?

It has often been claimed that the structure of export trade between Africa and Europe during the colonial period depended on the colonizer identity, with the British relying on free trade and the French employing instead monopsonistic policies. Yet, due to the lack of systematic data on colonial trade, this claim has so far remained untested. In this paper, I use recently available data on export prices from African colonies to estimate monopsonistic profit margins for British and French trading companies. The results challenge the view of the British colonizers as champions of free trade. The level of profit margins was determined much more by the local conditions in Africa than by the identity of the colonial power. The British did not necessarily rely on free trade more than the French and did so only when a stronger control of trade was not a viable option.
Competing risk model for dengue epidemiology in Sri Lanka: modeling length of stay in hospital

This paper focuses on exploring methods for analyzing length of stay in hospital and the discharge endpoint for dengue patients reported from high risk areas of Sri Lanka during the period of 2006-2008. Due to the length of stay being related to different endpoints, the response of length of stay is modeled with the concept of competing risk. A popular competing risk model of sub distribution proportional hazard model was fitted to find out the associated factors for different end points of the discharge competing risk model. Moreover this study concludes that the effect of age, ethnicity, dengue classification, district and platelet count are important to the discharge status of the competing risk model.
Multi-Class Detection of Abusive Language Using Automated Machine Learning

Abusive language detection online is a daunting task for moderators. We propose Automated Machine Learning (Auto-ML) to semi-automate abusive language detection and to assist moderators. In this paper, we show that multi-class classification powered by Auto-ML is successful in detecting abusive language in English and German as well as and better than the state-of-the-art machine learning models. We also highlight how we combatted the imbalanced data problem in our data-sets through feature selection and undersampling methods. We propose Auto-ML as a promising approach to the field of abusive language detection, especially for small companies who may have little machine learning knowledge and computing resources.
Respiratory Muscle Training Improves Functional Outcomes and Reduces Fatigue in Patients with Myasthenia Gravis: A Single-Center Hospital-Based Prospective Study

Background Myasthenia gravis (MG) is an immune-mediated disorder characterized by muscle fatigue and fluctuating weakness. Impairment in respiratory strength and endurance has been described in patients with generalized MG. We tested the hypothesis that respiratory muscle training (RMT) can improve functional outcomes and reduce fatigue in patients with MG. Methods Eighteen patients with mild to moderate MG participated in this study. The training group underwent home-based RMT three times a week for 12 weeks. Sixteen patients with MG without RMT were enrolled as a disease control group. Lung function, autonomic testing, Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF), and functional outcome measurement by using quantitative myasthenia gravis (QMG) score and myasthenia gravis composite (MGC) scale were measured before and after the 12-week RMT. Results The 12-week RMT significantly increased forced vital capacity (FVC) from 77.9 ± 12.6% to 83.8 ± 17.7% (p = 0.03), forced expiratory volume in one second (FEV1) from 75.2 ± 18.3% to 83.3 ± 19.0% (p = 0.03), forced expiratory volume in one second (FEV1) from 75.2 ± 18.3% to 83.3 ± 19.0% (p = 0.03), forced expiratory volume in one second (FEV1) from 75.2 ± 18.3% to 83.3 ± 19.0% (p = 0.03), forced expiratory volume in one second (FEV1) from 75.2 ± 18.3% to 83.3 ± 19.0% (p = 0.03), forced expiratory volume in one second (FEV1) from 75.2 ± 18.3% to 83.3 ± 19.0% (p = 0.03), forced expiratory volume in one second (FEV1) from 75.2 ± 18.3% to 83.3 ± 19.0% ( Conclusion The home-based RMT is an effective pulmonary function training for MG patients. The RMT can not only improve short-term outcomes but also reduce fatigue in patients with mild to moderate generalized MG.
Analysis of Emergent Behavior in Multi Agent Environments using Deep Reinforcement Learning

Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multi agent systems. We examine some of the factors that can inﬂuence the dynamics of the learning process in such a setting. We also analyze the effectiveness of few multi-agent reinforcement learning algorithms and present the results of extending popular single agent algorithms to multi-agent game environments. We also report some of the collective behavior that arose via interactions of individual agents to yield distinct targeted results at the group level.
Propagation of Aloysia citriodora Palau Using Different Cutting Types under Light Intensity

Aims: The aim of this study was to evaluate the effect of types of cuttings and light intensity on cidró vegetative propagation. Place and Duration of Study: Medicinal Plants Garden, Federal University of Grande Dourados (UFGD), Dourados, state of Mato Grosso do Sul, Brazil, September to November 2016. Methodology: The experiment was carried out under two light conditions (shaded and full light), evaluating three types of cuttings (softwood, semi-hardwood and hardwood). Experimental design was a 2 x 3 factorial randomized complete block design (RCBD) with four replications. Results: Hardwood cuttings showed the highest survival rates (86% and 82%), regardless of light. Highest fresh weight (0.6062 g plant -1 ) and dry mass (0.2987 g plant -1 ) with a leaf of 44.57 cm 2 were from hardwood cuttings, regardless of the light, while the longest root length were those of softwood cuttings under full light. Physiological indices varied as a function of light intensity, The Original Research Article Silva et al.; EJMP, 31(3): 1-9, 2020; Article no.EJMP. 54973 2 highest values of leaf area ratio (46.11 cm g) and specific leaf mass (0.0037 g cm) occurred in cuttings under full light. Conclusion: The vegetative propagation of cidró can be carried out using the hardwood portion of the cutting branch, under full light.
GESTATIONAL DIABETES MELLITUS: The Other Diabetes on the Rise

Over the years, there have been numerous reports of the escalation of diabetes worldwide. It has been estimated that in the United States, nearly 21 million children and adults have some form of diabetes, accounting for 7% of the population (1). Further, the American Diabetes Association (ADA) has estimated that 4% of pregnant women or approximately 135,000 women per year are diagnosed with gestational diabetes mellitus (GDM) (2). As a health and fitness professional, you will encounter women who have either previously been diagnosed with GDM or are at risk for developing GDM in current or future pregnancies. Unfortunately, there is up to a 63% chance of developing type 2 diabetes during her lifetime once a woman has been diagnosed with GDM (3). Thus, it is important to understand the GDM risk factors and what you, as a health and fitness professional, can do to help your clients prevent or manage their GDM through exercise. To prepare you for encounters with clients diagnosed with, or at risk for, GDM, this article will review the GDM screening and diagnosis procedures, discuss health risks associated with GDM, and provide you with dietary and exercise guidelines for preventing the onset of GDM and/or the progression of GDM to type 2 diabetes mellitus.
Comparison between Child-Pugh score and Albumin-Bilirubin grade in the prognosis of patients with HCC after liver resection using time-dependent ROC

Backgrounds The Child-Pugh score is a scoring system used to measure liver function and predict postoperative outcomes in patients with hepatocellular carcinoma (HCC). Recently, the Albumin-Bilirubin (ALBI) grade has been proposed for the evaluation of hepatic reserve function in HCC. This study aimed to assess and compare the capability of ALBI grade and Child-Pugh score in predicting overall survival (OS). Methods A total of 196 consecutive HCC patients who treated with hepatectomy were enrolled in this retrospective study. The prognostic values of ALBI grade and Child-Pugh score in predicting postoperative OS were respectively estimated using the Kaplan-Meier method and time-dependent receiver operating curves (ROC). Univariate and multivariate Cox regression analyses were performed to investigate the prognostic factors for OS. Results Stratified by the Albumin-Bilirubin (ALBI) system, there were 81 (41.3%) patients with grade 1 and 115 (58.7%) patients with grade 2. The cumulative 1-, 3-, 5-year OS rates in patients with ALBI-1 were 82.7%, 51.5% and 35.5%, respectively. For patients with ALBI-2, the cumulative 1-, 3-, 5-year OS rates were 57.6%, 19.4% and 0%, respectively. Based on the Child-Pugh classification, 136 (69.4%) patients had a score of 5, and 60 (30.6%) patients had a score of 6. Patients with Child-Pugh-A5 showed a better OS than those with Child-Pugh-A6, with respective OS at 1, 3 and 5 years (72.7%, 29.2%, 20.3% vs. 53.9%, 21.1%, 0%, Log-rank P<0.001). Besides, the ALBI grade revealed two prognostic groups within Child-Pugh-A5 (P<0.001), while the Child-Pugh score did not distinguish ALBI-2 in different prognostic groups (P=0.705). The multivariate analysis indicated that both ALBI grade and Child-Pugh score could significantly stratify the patients with different OS [hazard ratio (HR), 3.088 and 1.783; 95% confidence interval (CI), 1.985 to 4.805 and 1.272 to 2.731; P<0.001 and P=0.032 for ALBI grade and Child-Pugh score, respectively]. Additionally, time-dependent ROC analysis in the entire cohort proved that the ALBI grade had a better discriminatory ability than the Child-Pugh score in predicting survival, especially for long-term outcomes. According to the subgroup analyses, the ALBI grade had a better discriminatory ability and survival prediction accuracy in overall subsets than the Child-Pugh score for the prediction of OS. Conclusions ALBI grade supplied better prognostic performance and distribution of liver function than Child-Pugh score in stratifying prognosis for HCC patients treated by hepatectomy. These results declared that ALBI grade could be an alternative liver function grading system for stratification in patients with HCC.
Computational and Visual Tools for Geospatial Multi-Criteria Decision-Making

Geospatial multi-criteria decision-making usually concerns quasi-continuous choice models, with the number of alternatives constrained only by the limits of the used representation model. This sets high demands on the decision-making methods used in the context. The most commonly used approach in geospatial decision-making is combining a method for assigning criteria weights with an aggregation method. As pairwise comparison of alternatives is not feasible when the number of alternatives is large, the weights are usually assigned to criteria without considering the values or the value ranges of the alternatives, an approach often criticized in the decision analysis literature. Apart from criteria weighting controversy, this approach does not allow for advanced use of interactive visualization in the choice phase of the decision-making process. In this thesis, two alternative methods for geospatial decision-making based on the even swaps method are developed. The first method relies on automation of swaps, which makes this method viable for decision problems with any number of alternatives. The second method emanates from the findings of behavioral decision theory, and combines even swaps with reduction of large data sets through quasi-satisficing, allowing for efficient use of interactive visualization in the choice phase of the decision process. Visualization frameworks for both methods are also developed in the thesis. They include both geo-specific representations, such as interactive maps, and information visualization techniques such as graphs, diagrams, scatterplots and parallel coordinates. Two studies concerning the impact of interactive visualization on decision-making are presented in the thesis: a study concerning the impact of interactive visualization on geospatial decision-making, and a study concerning potential effects of visual saliency on decision-making. The results of the first study indicate positive effects of interactive visualization on coherency and consistency in performing trade-offs. The results of the second study suggest that saliency can increase attention and decision quality in MCDM for certain visualization techniques and forms of saliency. The work presented in this thesis contributes to method development and the use of interactive visualization in the context of geospatial decision-making.
Investigation of the Atmospheric Attenuation Factors in FSO Communication Systems Using the Taguchi Method

In this study, Mie and Rayleigh scattering in free space optics (FSO) communication systems were investigated in terms of the atmospheric attenuation. Because of the movement of the Earth, the communication distance and surrounding gas densities are inconsistent in each region. This change leads to atmospheric attenuation and then data losses and inefficient communication in FSO occur. Therefore, the density change and distance must be calculated in each communication once the data is transmitted. In the literature, it was observed that the atmospheric attenuation is regarding some FSO communication parameters such as transmission distance, visibility, and scatter particle size distribution, the number of particles per unit volume, scatter cross-sectional area, and wavelength. Besides, in real-time communication, it is necessary to update FSO parameters simultaneously. However, this updating process for all parameter takes a long time to adapt to a new position. This paper proposes the design of the experiment method (Doe) to determine the severity of the FSO parameters. And Taguchi’s Doe method allows analyzing of FSO communication system parameters to avoid long calculation time. Results show that the proposed method helps in understanding the priorities of the parameters in FSO and reducing the updating time.