Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Oil Tax Revenue on Economic Growth and Development in Nigeria

Globally, government is experiencing difficulties in generating adequate revenue to finance their activities. In Nigeria, studies have shown that the recent sharp reduction in the revenue was as a result of decline in crude oil prices which inversely affected the financial ability of government towards growth and development of the nation. To this end, this paper therefore investigated the effect of non-oil taxes on economic growth and development of Nigeria. The study employed ex-post facto research design. Macro data for the period 1994Q1-2017Q4 representing seventy six (76) observations were obtained from CBN statistical bulletin and National Bureau of Statistics. The documents were already exposed to the scrutiny of the appropriate regulatory agencies. The data were analyzed using descriptive and inferential statistics employing multiple regressions. The study discovered that non-oil taxes (custom and excise duties, capital gain tax, company income tax, tertiary education tax and value added tax) have significant effect on economic growth. (Adj. R2 = 0.75, F(5,71) = 213.43, p< . 0.05). The individual effects are also positive and statistically significant: (VAT- β = 8.011, t(76)= 2.802, ρ<0.05, CIT- β = 2.560, t(76)= 2.383, ρ<0.05, CED - β = 1.767, t(76)=3.092, ρ<0.05, CGT- β = 4.162, t(76)= 3.509, ρ<0.05, and TET- β = 0.161, t(76)= 2.443, ρ<0.05). This study concluded that non-oil taxes significantly influenced both economic growth and economic development in Nigeria. The study recommended that government must strive to sustain the current unflinching commitment towards improving non-oil tax revenue, ensure Tertiary Education Tax collected translates into real development and also ensure efficient utilization of tax payers’ money to boost non-oil tax revenue collection which will then lead to economic growth and development.
Calibration of the Swift-UVOT ultraviolet and visible grisms

We present the calibration of the Swift UVOT grisms, of which there are two, providing low-resolution field spectroscopy in the ultraviolet and optical bands respectively. The UV grism covers the range λ1700-5000 u with a spectral resolution (λ/�λ) of 75 at λ2600 u for source magnitudes of u=10-16 mag, while the visible grism covers the range λ2850-6600 u with a spectral resolution of 100 at λ4000 u for source magnitudes of b=12-17 mag. This calibration extends over all detector positions, for all modes used during operations. The wavelength accuracy (1-sigma) is 9 u in the UV grism clocked mode, 17 u in the UV grism nominal mode and 22 u in the visible grism. The range below λ2740 u in the UV grism and λ5200 u in the visible grism never suffers from overlapping by higher spectral orders. The flux calibration of the grisms includes a correction we developed for coincidence loss in the detector. The error in the coincidence loss correction is less than 20%. The position of the spectrum on the detector only affects the effective area (sensitivity) by a few percent in the nominal modes, but varies substantially in the clocked modes. The error in the effective area is from 9% in the UV grism clocked mode to 15% in the visible grism clocked mode .
Economic Potential of High Density Data Storage Implemented by Patterned Magnetic Media Technology

Hard drive industry is facing scaling challenge for areal density to be further increased. This is due to the triangular conflictions among thermal stability (superparamagnetic effect), single-to-noise ratio and writability of the recording media. One of the most promising methods to overcome this constraint is the patterned magnetic media technology. Although it is facing many challenges, the large potential gains in density offered by patterned media make it one of the possible milestones on the horizon for future of the disk drives industry. One of the biggest challenges for patterned media is to realize its mass fabrication provided reduced cost per bit. The basic fabrication approach is to use lithography to pattern the magnetic materials on the platter. However, patterned media requires well-ordered nanoarrays with dimensions less than 25 nm, which challenges the state-of-art lithography technologies. This M. Eng. project focuses on evaluations of the technologies and fabrication schemes potential for patterned media from various aspects like technical barriers, cost and intellectual properties. Technologies including E-beam lithography, nanoimprint lithography, templated diblock copolymer self-assembly and self-assembled magnetic nanoparticles are discussed. Cost modeling was done to prove the enormous gain in revenue for the proposed fabrication scheme. It is proposed that the fabrication scheme of templated diblock copolymer for making the master stamp for nanoimprint followed by nanoimprint lithography for mass production has the largest potential for patterned media. However, more R & D is needed for templated self-assembly of diblock copolymer before it is ready for this application. E-beam lithography which is a mature technology can also be a choice for making the stamp followed by mass production enabled by nanoimprint lithography, without a significant loss of gain in revenue for ultra-highdensity media fabrications. Although the cost of a master stamp fabricated by E-beam is estimated to be 50 times more than for templated self-assembly of diblock copolymer lithography. Thesis advisor: Caroline A. Ross Title: Professor of Materials Science and Engineering
Using a Vegetation Model and Stakeholder Input to Assess the Climate Change Vulnerability of Tribally Important Ecosystem Services

We demonstrate a generalizable approach for assessing climate change effects on tribally important ecosystem goods and services. Indigenous peoples may be highly vulnerable to the impacts of climate change because they rely on ecosystem goods and services, such as traditional foods, hunting, timber production, nontimber forest resources, and cultural resources. However, there are few assessments that have examined the potential impact of climate change on these goods and services and even less that examine ecological, socio-economic, and cultural resources in the Pacific Northwest, USA. Our approach uses four basic steps: (1) identify 78 tribally important ecosystem services (species and resources), (2) relate those ecosystem services with biologically relevant vegetation projections from a dynamic global vegetation model, (3) identify appropriate timeframes and future climate scenarios, and (4) assess future changes for vegetation types and ecosystem services. We then highlight how model uncertainty can be explored to better inform resilience building and adaptation planning. We found that more than half of the species and resources analyzed may be vulnerable to climate change due to loss of potential habitat, including aridland species and grazing quality. We further highlight our findings for tribally important species, huckleberries (genus Vaccinium) and bitterbrush (Purshia tridentate (Pursh) DC. ), and show how this information can be applied to help inform resource management and adaptation planning. We have demonstrated a generalizable approach that identified tribally important ecosystem services and related them with biologically relevant vegetation projections from a Dynamic Global Vegetation Model. Although our assessment is focused in the Pacific Northwest, our approach can be applied in other regions for which model data is available. We recognize that there is some inherent uncertainty associated with using model output for future scenario planning; however, if that uncertainty is addressed and applied as demonstrated by our approach, it then can be explored to help inform resource management and adaptation planning.
Determination of chemical composition and evaluation of Antioxidant, and Antimicrobial activities of Clove Oil obtained from Syzygium Aromaticum Moroccan species

The composition, antimicrobial and antioxidante activities of essential oil obtained from Syzygium aromaticum (Moroccan species), were studied. The extraction was carried out by hydro-distillation and analyzed by gas chromatography-mass spectrometryy (GC-MS). The antioxidant effect was evaluated using two in vitro antioxidant assay using DPPH and FRAP methods, 2,2-Diphenyil picrylhydrazyl free radical (DPPH) and Total reducing ability determination by Fe3+–Fe2+ transformation Method (FRAP). And the antimicrobial effects of essential oils were evaluated by the disk diffusion method. The determination of composition showed that Twenty-four compounds were identified in the extracts of clove oil by gas chromatographyy/mass spectrometry. The constituents major of clove buds was Eugenol with percentage (63, 68 %). The result shown that clove oil had an important effect antioxidant in two in vitro assays including reducing power FRAP and DPPH radical scavenging. The result has been compared to that of some natural antioxidant. Based on the measurement of the diameter of inhibition, a moderate to high antimicrobial activity, according to oils was revealed against all yeast strains and moderate against bactéria strains. The Eo has showed a considerable antioxidant effect and antimicrobial activity against bacterial and fungus strains, deserving further investigation for clinical application in thetreatment of fungal infections.
HSF-CWP-2017-05 March 25 , 2018 HEP Software Foundation Community White Paper Working Group – Data Analysis and Interpretation

At the heart of experimental high energy physics (HEP) is the development of facilities and instrumentation that provide sensitivity to new phenomena. Our understanding of nature at its most fundamental level is advanced through the analysis and interpretation of data from sophisticated detectors in HEP experiments. The goal of data analysis systems is to realize the maximum possible scientific potential of the data within the constraints of computing and human resources in the least time. To achieve this goal, future analysis systems should empower physicists to access the data with a high level of interactivity, reproducibility and throughput capability. As part of the HEP Software Foundation’s Community White Paper process, a working group on Data Analysis and Interpretation was formed to assess the challenges and opportunities in HEP data analysis and develop a roadmap for activities in this area over the next decade. In this report, the key findings and recommendations of the Data Analysis and Interpretation Working Group are presented.
Wavelet characterisation of ionospheric acoustic and gravity waves occurring during the solar eclipse of August 11 , 1999

We propose here a wavelet-based detection and characterisation of the propagation of acoustic-gravity waves induced in the ionosphere by the solar eclipse of August 11, 1999. The data are obtained from measurements of electron density profiles using (1min vertical incidence) ionospheric sounding performed at Pruhonice (Czech Republic, 49.9N, 14.5E) and consist of the fluctuations of electron concentrations. The main goals of the current work are twofold. First, we describe in details the wavelet-based methodology used to detect and characterise acoustic and gravity waves and explain how and why this improves the previously proposed Fourier based approaches. Mainly, we show that wavelet transforms allow us to untangle waves that would be otherwise mixed up in Fourier decompositions. Second, we study carefully different waves and characterise them in terms of time of occurrence, period, wave vector, packet and phase velocities. We also demonstrate the existence of acoustic waves during the solar eclipse and characterise accurately. Such detections are made possible by the combined use of both data with a high sampling rate (1min) and wavelet-based tools. r 2005 Elsevier Ltd. All rights reserved.
MANUFACTURING AND AEROSTATIC TUNABILITY OF OPTO-MECHANO-FLUIDIC RESONATOR AND ITS APPLICATION IN VISCOSITY SENSING BY KEWEN HAN THESIS

1 2 Cavity optomechanics experiments parametrically couple the phonon modes and photon modes in microresonators and various optical systems have been investigated. However, because of the increased acoustic radiative losses during direct liquid immersion of optomechanical devices, almost all published optomechanical experiments have been performed in solid phase. The high acoustic losses during direct liquid immersion limits the biosensing applications of optomechanical devices. This thesis discusses a recently introduced hollow opto-mechano-fluidic resonator (OMFR), which by design are equipped for microfluidic experiments. By confining liquids inside the capillary resonator, high mechanicaland opticalquality factors are simultaneously maintained. Unlike optofluidics biosensing, because the optical modes don’t interact with the liquids directly, the optomechanical biosensing doesn’t have any requirement of the optical properties of the fluids and bioanalytes. Detailed methodology is provided to fabricate these ultra-high-Q microfluidic resonators, perform optomechanical testing, and measure radiation pressure-driven breathing mode (10–20 MHz) and SBS-driven (10–12 GHz) whispering gallery mode vibrations. We also experimentally investigate aerostatic tuning of these hollow-shell oscillators, enabled by geometry, stress, and temperature effects. We demonstrate for the first time the simultaneous actuation of RP-induced breathing mechanical modes and SBSinduced whispering gallery acoustic modes, through a single pump laser. In addition, we show that fluid viscosity can also be determined through optomechanical measurement of the vibrational noise spectrum of the resonator This thesis includes the author’s previously published material [1–4]. The copyright owners have provided permission to reprint. 2 [2] was published in Optics Express and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://www.opticsinfobase. org/oe/abstract.cfm? uri=oe-22-2-1267. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.
Information visualisation and machine learning: latest trends towards convergence

Many methods have been developed in machine learning (ML) for information visualisation (infovis). For example, PCA, MDS, t-SNE and improvements are standard tools to reduce the dimensionality of high dimensional datasets for visualisation purposes. However, multiple other means are regularly used in the ﬁeld of infovis when tackling datasets with high dimensionality. Letting the user manipulate the visualisation is one of these means, either through selection, navigation or ﬁltering. Introducing manipulation of the visualisation also integrates the user as a core aspect of a given system. In the context of machine learning, beyond the informational and exploratory use of infovis, users’ feedback can for example be highly informational to drive the dimensionality reduction process. This special session of the ESANN conference is a followup of the special session on ”Information Visualisation and Machine Learning: Techniques, Validation and Integration” at ESANN 2016. It aims to gather researchers that integrate users in the core of ML methods for infovis. New algorithms and frameworks are welcome, as well as experimental use cases that bring new insight in the integration of interaction and user integration in ML for infovis. This special session aims to provide practitioners from both communities a common forum of discussion where issues at the crossroads of machine learning and information visualisation could be discussed.
Self-supported carbon electrodes obtained by tape casting

This paper describes the preparation and electrochemical response of self-supported carbon electrodes prepared by tape casting. The dc electrical conductivity, σ, of the electrodes was determined by four-wire resistance measurements and a relation between the graphite/organic additives ratio and the electrical conductivity was established. The application of these self-supported carbon electrodes as working electrodes in analytical techniques was also evaluated using norepinephrine as electroactive substance in cyclic voltammetry and chronoamperometry. The results were compared with the traditional electrodes, carbon paste electrodes (CPEs), showing that the new self-supported carbon electrodes had both lower background noise and higher analytical response.
Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods

Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance.
Networked Reality A Taxonomy of Mixed Reality Visual Displays

This paper focuses on Mixed Reality (MR) visual displays, a particular subset of Virtual Reality (VR) related technologies that involve the merging of real and virtual worlds somewhere along the “virtuality continuum” which con nects completely real environments to completely virtual ones. Probably the best known of these is Augmented Reality (AR), which refers to all cases in which the display of an otherwise real environment is augmented by means of virtual (computer graphic) objects. The converse case on the virtuality continuum is therefore Augmented Virtuality (AV). Six classes of hybrid MR display environments are identified. However, an attempt to distinguish these classes on the basis of whether they are primar ily video or computer graphics based, whether the real world is viewed directly or via some electronic display medium, whether the viewer is intended to feel part of the world or on the outside looking in, and whether or not the scale of the display is intended to map orthoscopically onto the real world leads to quite different groupings among the six identified classes, thereby demonstrating the need for an efficient taxonomy, or classification framework, according to which essential differences can be identified. The ‘obvious’ distinction between the terms “real” and “virtual” is shown to have a number of different aspects, depending on whether one is dealing with real or virtual objects, real or virtual images, and direct or non-direct viewing of these. An (approximately) three dimensional taxonomy is proposed, comprising the following dimensions: Extent of World Knowledge (“how much do we know about the world being displayed? ”), Reproduction Fidelity (“how ‘realistically’ are we able to display it? ”), and Extent of Presence Metaphor (“what is the extent of the illusion that the observer is present within that world? ”). key words: virtual reality (VR), augmented reality (AR), mixed reality (MR)
An Empirical Analysis on Spanish Firm Level Data

A short review of the theoretical and empirical evidence indicates that Foreign Direct Investment (FDI) has the potential to increase the intensity of competition as well as to act as a channel for technology transfers. One would expect, all else equal, an increase in average productivity following a wave of FDI, as multinational corporations (MNCs) enjoy higher levels of e¢ciency. At the same time, the entry of foreign . rms has also been associated with an increase in competitive pressure on the domestic market. Using a large . rm level data set covering all sectors of Spanish manufacturing during the period 1983-1996, we attempt to disentangle these two e¤ects by estimating a dynamic model of . rm level pro.tability. We . nd that FDI has a positive long-run e¤ect on the pro.tability of target . rms, but this is limited to . rms belonging to R&D intensive sectors. In addition, the results indicate that foreign presence dampens margins. However, this e¤ect appears to be more than compensated by positive spillovers in the case of knowledge intensive industries. JEL Codes: F23; L40; L60.
Joint Representations of Knowledge Graphs and Textual Information via Reference Sentences

SUMMARY The joint representations of knowledge graph have become an important approach to improve the quality of knowledge graph, which is beneﬁcial to machine learning, data mining, and artiﬁcial intelligence applications. However, the previous work su ﬀ ers severely from the noise in text when modeling the text information. To overcome this problem, this paper mines the high-quality reference sentences of the entities in the knowledge graph, to enhance the representation ability of the entities. A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is proposed, which embeds the entity, the relations, and the words into a uniﬁed vector space. The proposed framework consists of knowledge graph representation learning module, textual relation representation learning module, and textual entity representation learning module. Experiments on entity prediction, relation prediction, and triple classiﬁcation tasks are conducted, results show that the proposed framework can signiﬁcantly improve the performance of mining and fusing the text information. Especially, compared with the state-of-the-art method[15], the proposed framework improves the metric of H@10 by 5.08% and 3.93% in entity prediction task and relation prediction task, respectively, and improves the metric of accuracy by 5.08% in triple classiﬁcation task.
Can local be the new organic? Food choice motives and willingness to pay

Due to growth and changing distribution channels for organic food in Germany, there is some concern that organic food will lose against local food in the competition for conscious consumers. In this paper we will present the results of a survey in Bavaria searching for consumer motives and label recognition. A choice experiment using different prices, brands and labels is conducted for bread, beer and milk. Results show the importance of local production to the surveyed consumers, similarly for conventional as for organic products.