Miracle-dz/UniFAR-FTUs huggingface dataset:
Level of Education and Economic Growth in the Member States of the European Union: A Comparative Analysis

RESEARCH OBJECTIVE: The aim of the article is to present the results of the analysis of the education level in the European Union countries and to verify the relationship between the education and the economic growth of the studied countries. THE RESEARCH PROBLEM AND METHODS: The basic problem of this publication is quantitative and comparative analysis of the level of education in the European Union countries (EU27). Research methods used in the paper are the analysis and synthesis of the literature on the subject as well as quantitative analysis of the education using statistical and taxonomic tools (descriptive statistics, cluster analysis, taxonomic measure of education [TME]). An analysis of regression and correlation is also conducted to investigate the relationship between education and economic growth. THE PROCESS OF ARGUMENTATION: The line of reasoning consists of three essential elements. The first part presents the issues of education and economic growth in the light of the theory. This is a selective review of literature. The second part contains a comprehensive description of the research tools and methods. Part three of the article presents the results of the investigation in the field of education and its relationship with economic growth. The level of education was determined on the basis of an aggregated measure constructed from diagnostics variables. GDP per capita was used to estimate economic growth. The papers ends with conclusion and recommendations for future research. RESEARCH RESULTS: On the basis of theoretical considerations, the influence of knowledge and education on economic growth has been confirmed, particularly in the models of endogenous growth. In empirical research, a great part of the research also indicates a positive relationship between education and economic growth. There are also elaborations that do not confirm this correlation. This study provides two important information on education and economic growth in the Member States of the European Union. Firstly, the area of the European Union is very differentiated taking into account the two criteria indicated. Over the years, a gradual reduction in disparities is observed both in GDP per capita and in TME. Secondly, quantitative analysis has confirmed the emerging relationship between education and economic growth. CONCLUSIONS, INNOVATIONS AND RECOMMENDATIONS: The issue of relationship between education and economic growth is very important and timely. There is lack of papers in the literature that use taxonomic methods to evaluate education. These tools give great opportunity to present the problem in a multidimensional and comprehensive manner, taking into account a large number of variables. The research should be treated as a preliminary study in this field, which requires further in-depth investigation. It is recommended to include more diagnostic variables related to education, as well as, more advanced research techniques in the fields of statistics, econometrics and taxonomy.
An Effective Directional Residual Interpolation Algorithm for Color Image Demosaicking

In this paper, we propose an effective directional Bayer color filter array (CFA) demosaicking algorithm based on residual interpolation (RI). The proposed directional interpolation algorithm aims to reduce computational complexity and get more accurate interpolated pixel values in the complex edge areas. We use the horizontal and vertical weights to combine and smooth color difference estimations. Compared with four directional weights in minimized Laplacian residual interpolation, the proposed algorithm not only guarantees the quality of color images but also reduces the computational complexity. Generally, the directional estimations may be inaccurately calculated because of the false edge information in irregular edges. We alleviate it by using a new method to calculate the directional color difference estimations. Experimental results show that the proposed algorithm provides outstanding performance compared with some previous algorithms, especially in the complex edge areas. In addition, it has lower computational complexity and better visual effect.
Wakefields - An Overview

In this paper, we will report on the basic concepts of ultra-relativistic wake-ﬁelds and their effects on particles while focusing; in this context, particularly on particle accelerators. We will introduce the commonly used terminology, and will derive and explain important quantities, such as the wake potential and wake function, the impedance as the Fourier transform of the wake potential, and loss parameters. To deepen knowledge on wake functions and potentials further, we will illustrate the derived quantities using, as examples, the cylindrical cavity and a rectangular waveguide lined with dielectrics.
Community Coalition Kick-Off Events: Lessons Learned on the Launchpad

Research has found that collaboration is important for family service agencies, and community coalition building is a common form of collaboration. Guided by Community Coalition Action Theory, this article analyzes a community coalition convening around health and housing in a resource-challenged, large Midwestern metropolitan area. Implications for practice are discussed, describing initial lessons learned from the coalition kick-off event for community coalition convening. Using theory to anticipate coalition challenges and barriers, looking beyond simple meeting agenda objectives to identify specific coalition tactics, and asking hard questions about coalition capacity at kick-off are ways to strengthen health and housing interventions within family-based services.
Multilabel classification of remote sensed satellite imagery

Multilabel scene classification has emerged as a critical research area in the domain of remote sensing. Contemporary classification models primarily emphasis on a single object or multiobject scene classification of satellite remote sensed images. These classification models rely on feature engineering from images, deep learning, or transfer learning. Comparatively, multilabel scene classification of very high resolution (V.H.R.) images is a fairly unexplored domain of research. Models trained for single label scene classification are unsuitable for the application of recognizing multiple objects in a single remotely sensed V.H. R. satellite image. To overcome this research gap, the current inquiry proposes to fine‐tune the state of the art convolutional neural network (C.N.N.) architectures for multilabel scene classification. The proposed approach pre trains C.N. N on the ImageNet dataset and further fine‐tunes them to the task of detecting multiple objects in V.H. R. images. To understand the efficacy of this approach, the final models are applied on a V.H. R. dataset: the U.C. M.E. R.C. E.D. image dataset containing 21 different terrestrial land use categories with a submeter resolution. The performance of the final models is compared with graph convolutional network‐based model by Khan et al. From the results on performance metrics, it was observed that proposed models achieve comparable results in significantly fewer epochs.
Critical Evaluation of Organic Thin-Film Transistor Models

The thin-film transistor (TFT) is a popular tool for determining the charge-carrier mobility in semiconductors, as the mobility (and other transistor parameters, such as the contact resistances) can be conveniently extracted from its measured current-voltage characteristics. However, the accuracy of the extracted parameters is quite limited, because their values depend on the extraction technique and on the validity of the underlying transistor model. We propose here a new approach for validating to what extent a chosen transistor model is able to predict correctly the transistor operation. In the two-step fitting approach we have developed, we analyze the measured current-voltage characteristics of a series of TFTs with different channel lengths. In the first step, the transistor parameters are extracted from each individual transistor by fitting the output and transfer characteristics to the transistor model. In the second step, we check whether the channel-length dependence of the extracted parameters is consistent with the underlying model. We present results obtained from organic TFTs fabricated in two different laboratories using two different device architectures, three different organic semiconductors and five different materials combinations for the source and drain contacts. For each set of TFTs, our approach reveals that the state-of-the-art transistor models fail to reproduce correctly the channel-length-dependence of the transistor parameters. Our approach suggests that conventional transistor models require improvements in terms of the charge-carrier-density dependence of the mobility and/or in terms of the consideration of uncompensated charges in the carrier-accumulation channel.
Knowledge-sharing efforts and employee creative behavior: the invigorating roles of passion for work, time sufficiency and procedural justice

Drawing from the conservation of resources theory, this study aims to investigate the relationship between employees’ knowledge-sharing efforts and creative behaviors; particularly, it addresses how this relationship may be invigorated by three resources that operate at individual (passion for work), job (time sufficiency) and organizational (procedural justice) levels. ,Quantitative data were collected through a survey administered to employees in a banking organization in Mozambique. ,The usefulness of knowledge-sharing efforts for stimulating creative behavior is greater when employees feel passionate about work, have sufficient time to complete their job tasks and perceive that organizational decision-making is fair. ,The results inform organizations about the circumstances in which the application of employees’ collective knowledge bases, derived from their peer interactions, to the generation of novel solutions for problem situations is more likely to materialize. ,By detailing the interactive routes by which knowledge-sharing efforts and distinct resources (passion for work, time sufficiency and procedural justice) promote employee creative behavior, this study extends prior research that has focused on the direct influences of these resources on knowledge sharing and creative work outcomes. It pinpoints the circumstances in which intra-organizational knowledge exchange can generate the greatest value, in terms of enhancing creativity.
Smartphone Shadow Matching for Better Cross-street GNSS Positioning in Urban Environments

Global Navigation Satellite System (GNSS) shadow matching is a new positioning technique that determines position by comparing the measured signal availability and strength with predictions made using a three-dimensional (3D) city model. It complements conventional GNSS positioning and can significantly improve cross-street positioning accuracy in dense urban environments. This paper describes how shadow matching has been adapted to work on an Android smartphone and presents the first comprehensive performance assessment of smartphone GNSS shadow matching. Using GPS and GLONASS data recorded at 20 locations within central London, it is shown that shadow matching significantly outperforms conventional GNSS positioning in the cross-street direction. The success rate for obtaining a cross-street position accuracy within 5 m, enabling the correct side of a street to be determined, was 54·50% using shadow matching, compared to 24·77% for the conventional GNSS position. The likely performance of four-constellation shadow matching is predicted, the feasibility of a large-scale implementation of shadow matching is assessed, and some methods for improving performance are proposed. A further contribution is a signal-to-noise ratio analysis of the direct line-of-sight and non-line-of-sight signals received on a smartphone in a dense urban environment.
Cycle and Income-Level Convergence in the EU Countries: An Identification of Turning Points Based on the Hidden Markov Models

This paper aims to identify turning points in the economic growth paths of the European Union countries on the basis of the Hidden Markov Models. These turning points are then introduced as structural breaks in the ? -convergence model. Such an approach combines the analysis of cyclical and income-level convergence. Quarterly GDP time series for the whole EU28 group has two turning points (in 2008 and 2013). The hypothesis about the ? -convergence was successfully verified, but the ? -convergence process had a different pace between the turning points, being the slowest (or not even existing) one in the 2008–2012 subperiod, i.e. during the global crisis.
A simplified mechanistic model for predicting the critical incipient velocity of cuttings in inclined pipes

By the analysis of cuttings transport mechanism, a simplified mechanistic model for predicting the critical incipient velocity of cuttings in the inclined pipe was presented in this paper based on the balance of force and moment. Drag coefficient calculation method was introduced into the model to improve the prediction accuracy. The incipient movement of different cuttings at certain angle was observed under different flow rate of water and glycerol solution. The numerical results of present model are in good agreement with experimental data and both of which indicate the domination of suspension transport mechanism at low inclination angle, and the domination of rolling transport mechanism in the range of medium angle to horizontal. By the analysis of rolling transport velocity and suspension transport velocity of the particle in X-Y axis, both of which are calculated by the model, we can get the change law of critical incipient velocity of cuttings in inclined pipes. In particular, the maximum critical incipient velocity of cuttings was reached at about 60°of inclination while the minimum value is reached at lower inclination angle. The position of minimum value was determined by drag coefficient, lift coefficient and inclination angle.
Negative Consequences of Anthropomorphized Technology: A Bias-Threat-Illusion Model

Attributing human-like traits to information technology (IT) — leading to what is called anthropomorphized technology (AT)—is increasingly common by users of IT. Previous IS research has offered varying perspectives on AT, although it primarily focuses on the positive consequences. This paper aims to clarify the construct of AT and proposes a “bias–threat–illusion” model to classify the negative consequences of AT. Drawing on “threefactor theory of anthropomorphism” from social psychology and integrating self-regulation theory, we propose that failing to regulate the use of elicited agent knowledge and to control the intensified psychological needs (i.e., sociality and effectance) when interacting with AT leads to negative consequences: “transferring human bias,” “inducing threat to human agency,” and “creating illusionary relationship. ” Based on this bias–threat–illusion model, we propose theory-driven remedies to attenuate negative consequences. We conclude with implications for IS theories and practice.
Initiation into Alcohol Use in East and West German Adolescents.

This paper investigates the patterning and positioning effects in the onset of drinking in East and West German adolescents and young adults. Differences between the timing of first drinking (positioning effects) and differences in influences on the timing of initiation (patterning effects) are studied. Four reasons for studying the age of onset of drinking are given: low frequency of problem use, risk factor of later misuse, risk factor of later legal and illegal drugs, and cohort effects. Risk factors related to age at onset of drinking and previous findings are reviewed. Cultural differences between East and West Germans that influence the early onset of alcohol use are discussed, and gender differences are investigated. Two samples of 13 to 29 year olds were gathered in both East and West Germany, one in 1991 (N=3,824) and the other in 1996 (N=3,086) . A Cox Regression analysis (hazard model) was used in analysis of positioning effects (i.e., the transition from no experiences of drinking to the first whole drink). The investigation of timing or patterning effects utilized measures of parental variables, peer variables, and pubertal timing. Differences between East and West German samples are presented in five tables and two figures and discussed. Correlation of the age of onset with reported frequency of use is presented. Interaction effects and possible differences between the 1991 and 1996 samples are explored. (Contains 29 references.) (EMK) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** Initiation into Alcohol Use in East and West German Adolescents Martin Pinquart J. Gowert Masche U.S. DEPARTMENT OF EDUCATION . Offace of Educatenal Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 0 Thm document hae been reproduced as received from the person or organizahon . originating a Minor changes have been made to improve reproduction quality. 0 Points Of view or opinions stated in this dcc ument do not necessarily represent official OERI position or pohcy.
Systematic design of photonic crystal structures using topology optimization: Low-loss waveguide bends

Topology optimization is a promising method for systematic design of optical devices. As an example, we demonstrate how the method can be used to design a 90° bend in a two-dimensional photonic crystal waveguide with a transmission loss of less than 0.3% in almost the entire frequency range of the guided mode. The method can directly be applied to the design of other optical devices, e.g., multiplexers and wave splitters, with optimized performance.
A maximum power point tracking based on levy flight optimization

This paper proposes a Levy flight global maximum power point tracking for solar photovoltaic (PV) system under partial shading conditions. The proposed method comes with merits such as simplicity, fast response and free of oscillation. This algorithm uses random search over the exploration space and compares the previous and current states to obtain the best solution. For evaluation and comparative analysis, performance of the proposed method is also measured against Perturb and Observe (P&O) and Particle Swarm Optimization (PSO). All three algorithms are simulated in MATLAB/Simulink environment. Simulation results are satisfactory over the conducted tests under uniform and non-uniform irradiance. The proposed algorithm is able to track global maximum power point (GMPP) under partial shading conditions with fast tracking time and zero ripple at steady-state.
Adolescent sexual risk-taking behavior in single-parent ethnic minority families.

Relationships of maternal sexual behavior, mother-adolescent communication about sex, and maternal attitudes about adolescent sexuality to adolescent sexual risk-taking behavior were examined in a sample of 397 Black and Hispanic families headed by single mothers. Some support emerged for a positive relationship between maternal sexual risk-taking behavior and adolescent risk-taking behavior; however, when considered in the context of communication about sex and maternal attitudes about adolescent sexuality, the relationship was no longer significant. When the process of sexual communication between a mother and an adolescent was open and receptive, less adolescent risk-taking behavior was reported. The role of single mothers in influencing their adolescents' sexual behavior is discussed.
Wrapper-Based Acoustic Group Feature Selection for Noise-Robust Automatic Sleepiness Classification

This paper presents a noise-robust Wrapper-based acoustic Group Feature Selection (W-GFS) method and its large noise Optimized (OW-GFS) version for automatic sleepiness classification and compares their performances with Correlation-based Feature Selection (C-FS) and Pearson Correlation Coefficient Feature Selection (CC-FS) filters. We use Interspeech 2011 Speaker State Challenge’s “Sleepy Language Corpus” and baseline feature set. Group Feature Selection (GFS) considers the feature space in Low Level Descriptor groups rather than individually. Reduced time-complexity and potential generalization power of GFS are discussed. A model to predict on test data with changing Signal-to-Noise Ratio (SNR) is presented based on results from artificially corrupted development data with 10 dB SNR white-noise. Using Support Vector Machine, W-GFS achieves 2.6%, 4.2%, and 1.9% relative Unweighted Average Recall (UAR) improvement over the C-FS, CC-FS, and baseline feature set systems, respectively, on white-noise corrupted test data with randomly changing SNR within a broad range. The corresponding improvements for OW-GFS, using Voted Perception, are 4.8%, 9.8%, and 2.2% relative UAR on strongly whitenoise corrupted test data with randomly changing SNR between -5 and +5 dB. Finally, we discuss consistent results obtained using everyday environment noises.
An Approach for Visualization of Big Data Using Nanocubes

The concept of big data begins with the enormous amount of the data after the interpretation of the data warehousing. The practices performed in big data are generally related to issues in the large volumes of data or the unstructured data. The Visualization and structuring of data are the essential techniques required over the data available in the various domains in large amount. There are various techniques which can portray, coordinate, observe and browse the available data. Specially, the visualization of data techniques is associated with the understanding of the available data to plan the structuring of data as well as the analysis of the given databases. In this paper, visualization technique based upon the nanocubes concept is presented, which divide the data into smaller chunks, which make the data more understandable. In this paper, an improved system for the crime data visualization is depicted which consisted of the various layers altogether in order to process the input data and to produce the r esults in the form of the patterns, color maps, statistics and other such parametric values or the visualization techniques. KeywordsBig Data, Visualization, Nanocubes.
Decaying Swirl Flow and Particle Behavior through the Hole Cleaning Device for Horizontal Drilling of Fossil Fuel

The hole cleaning device is a powerful application which can effectively slow down the deposition of cuttings during drilling. However, in this complicated swirl flow created by the device, the decay of the swirl flow and the particle behavior are not evident yet. In this paper, the decay of the swirl flow and the particle behavior in the swirl flow field are studied by the Eulerian–Eulerian two-fluid model (TFM) coupled with the kinetic theory of granular flows (KTGF), and sliding mesh (SM) technique for simulating the fluid flow. The results show that the swirl intensity decays exponentially along the flow direction under laminar flow conditions. The swirl flow has a longer acting distance at a higher rotational speed, which can effectively slow down the deposition of cutting particles. The initial swirl intensity of swirl flow induced by the blades increases significantly with the increase of blade height and the decrease of the blade angle. The tangential velocity of the cutting particles in the annulus is more significant near the central region, gradually decreases toward the wall in the radial direction, and rapidly decreases to 0 at the wall surface. The decay rate is negatively correlated with the initial swirl intensity. The results presented here may provide a useful reference for the design of the hole cleaning device.