Miracle-dz/UniFAR-FTUs huggingface dataset:
Ellerman bombs and UV bursts: reconnection at different atmospheric layers

The emergence of magnetic flux through the photosphere and into the outer solar atmosphere produces, amongst other dynamical phenomena, Ellerman bombs (EBs), which are observed in the wings of Hα and are due to magnetic reconnection in the photosphere below the chromospheric canopy. Signs of magnetic reconnection are also observed in other spectral lines, typical of the chromosphere or the transition region. An example are the ultraviolet (UV) bursts observed in the transition region lines of Si IV and the upper chromospheric lines of Mg II. In this work we analyze high-cadence, high-resolution coordinated observations between the Swedish 1m Solar Telescope (SST) and the Interface Region Imaging Spectrograph (IRIS) spacecraft. Hα images from the SST provide us with the positions, timings, and trajectories of EBs in an emerging flux region. Simultaneous, co-aligned IRIS slit-jaw images at 133 (C II, transition region), 140 (Si IV, transition region), and 279.6 (Mg II k, core, upper chromosphere) nm as well as spectroscopy in the far- and near-ultraviolet from the fast spectrograph raster allow us to study the possible chromospheric and transition region counterparts of those EBs. Our main goal is to study the possible temporal and spatial relationship between several reconnection events at different layers in the atmosphere (namely EBs and UV bursts), the timing history between them, and the connection of these dynamical phenomena to the ejection of surges in the chromosphere. We also investigate the properties of an extended UV burst and their variations across the burst domain. Our results suggest a scenario where simultaneous and co-spatial EBs and UV bursts are part of the same reconnection system occurring sequentially along a vertical or nearly vertical current sheet. Heating and bidirectional jets trace the location where reconnection takes place. These results support and expand those obtained from recent numerical simulations of magnetic flux emergence.
Free energy of the self-interacting relativistic lattice Bose gas at finite density

The density of state approach has recently been proposed as a potential route to circumvent the sign problem in systems at finite density. In this study, using the Linear Logarithmic Relaxation (LLR) algorithm, we extract the generalised density of states, which is defined in terms of the imaginary part of the action, for the self-interacting relativistic lattice Bose gas at finite density. After discussing the implementation and testing the reliability of our approach, we focus on the determination of the free energy difference between the full system and its phase-quenched counterpart. Using a set of lattices ranging from $4^4$ to $16^4$ , we show that in the low density phase, this overlap free energy can be reliably extrapolated to the thermodynamic limit. The numerical precision we obtain with the LLR method allows us to determine with sufficient accuracy the expectation value of the phase factor, which is used in the calculation of the overlap free energy, down to values of ${\cal O}(10^{-480})$. When phase factor measurements are extended to the dense phase, a change of behaviour of the overlap free energy is clearly visible as the chemical potential crosses a critical value. Using fits inspired by the approximate validity of mean-field theory, which is confirmed by our simulations, we extract the critical chemical potential as the non-analyticity point in the overlap free energy, obtaining a value that is in agreement with other determinations. Implications of our findings and potential improvements of our methodology are also discussed.
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition

Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its environment. This led to breakthroughs in many complex tasks that were previously difficult to solve. However, deep RL requires a large amount of training time that makes it difficult to use in various real-life applications like human-computer interaction (HCI). Therefore, in this paper, we study pre-training in deep RL to reduce the training time and improve the performance in speech recognition, a popular application of HCI. We achieve significantly improved performance in less time on a publicly available speech command recognition dataset.
Interpretable Text Classification Using CNN and Max-pooling

Deep neural networks have been widely used in text classification. However, it is hard to interpret the neural models due to the complicate mechanisms. In this work, we study the interpretability of a variant of the typical text classification model which is based on convolutional operation and max-pooling layer. Two mechanisms: convolution attribution and n-gram feature analysis are proposed to analyse the process procedure for the CNN model. The interpretability of the model is reflected by providing posterior interpretation for neural network predictions. Besides, a multi-sentence strategy is proposed to enable the model to beused in multi-sentence situation without loss of performance and interpret ability. We evaluate the performance of the model on several classification tasks and justify the interpretable performance with some case studies.
Galaxy Rotation Curves in the µ-Deformation Based Approach to Dark Matter

We elaborate further the м-deformation-based approach to the modeling of dark matter, in addition to the earlier proposed use of м-deformed thermodynamics. Herein, we construct м-deformed analogs of the Lane–Emden equation (for density profiles) and find their solutions. Using these, we plot the rotation curves for a number of galaxies. Different curves describing the chosen galaxies are labeled by respective (different) values of the deformation parameter м. As a result, the use of м-deformation leads to the improved agreement with observational data. For all the considered galaxies, the obtained rotation curves (labeled by м) agree better with data, as compared to the well-known Bose–Einstein condensate model results of T. Harko. Besides, for five of the eight cases of galaxies, we find a better picture for rotation curves, than the corresponding Navarro–Frenk–White (NFW) curves. The possible physical meaning of the parameter м basic for this version of м-deformation is briefly discussed.
Hyperspectral terahertz microscopy via nonlinear ghost imaging

We experimentally demonstrate Time-Resolved Nonlinear Ghost Imaging and its ability to perform hyperspectral imaging in difficult-to-access wavelength regions, such as the Terahertz domain. We operate by combining nonlinear quadratic sparse generation and nonlinear detection in the Fourier plane. We demonstrate that traditional time-slice approaches are prone to essential limitations in near-field imaging due to space-time coupling, which is overcome by our technique. As a proof-of-concept of our implementation, we show that we can provide experimental access to hyperspectral images completely unrecoverable through standard fixed-time methods.
Explicit and Implicit Collocation Teaching Methods: Empirical Research and Issues

The need for integrating collocations into English language syllabuses in order to enhance EFL/ESL learners’ language accuracy and fluency has been emphasized by a great number of researchers and scholars. Given this, finding viable collocation teaching methods has become the focal center of some specialists’ interest. This has led into the emergence of various pedagogical suggestions some of which have aroused controversy. One of the most significant points of disagreement between these specialists concerns the effectiveness of implicit versus explicit collocation teaching methods. This article reviews the pedagogical potential of these two teaching approaches as well as the issues limiting generalizability of findings in the related literature. The review reveals that the scarcity of empirical studies which have compared the efficacy of these two controversial methods, lack of consistency in the reported findings, and neglect of some determining factors, such as learners’ individual differences, have caused a great deal of confusion in the literature regarding the most effective pedagogical intervention. Further research is, therefore, required to address these gaps in the realm of collocation and help teachers, instructional designers and material developers effectively support learners in the learning process. Some suggestions for future research are provided.
Optimisation of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method

In this paper, the use of the grey relational analysis based on an orthogonal array and fuzzy-based Taguchi method for optimising the multi-response process is reported. Both the grey relational analysis method without using the S/N ratio and fuzzy logic analysis are used in an orthogonal array table in carrying out experiments for solving the multiple responses in the electrical discharge machining (EDM) process. Experimental results have shown that both approaches can optimise the machining parameters (pulse on time, duty factor, and discharge current) with considerations of the multiple responses (electrode wear ratio, material removal rate, and surface roughness) effectively. It seems that the grey relational analysis is more straightforward than the fuzzy-based Taguchi method for optimising the EDM process with multiple process responses.
Psychological and Ethical Aspects of Living Kidney Donation

textabstractThe preferable treatment option for patients with end stage renal failure is a kidney transplantation. However, there is a shortage of deceased donor kidneys for transplantation. In the Netherlands, average waiting times for deceased donor kidney transplantation have increased, and range from 2 years for patients with blood type AB to 5 years for patients with blood type O. Patients waiting for a deceased donor kidney are dependent on dialysis treatment. Although dialysis is a life saving treatment, patients on dialysis are confronted with lowered quality of life and an increased risk of morbidity and mortality: approximately 25% of all patients die while waiting for a transplant. Living kidney donation offers a realistic alternative to patients with end stage renal failure. In living kidney donation, a living donor donates one of his/her kidneys to the patient. Donor risks for potential life threatening or severe complications are reported to be approximately 0.2% , and donor mortality risks are estimated at 0.03%. The quality of life of living donors after donation is likely to return to pre-donation levels , and is reported to be even higher than that of the general population. Since the first living kidney donation from a mother to her son in the Netherlands in 1966, the number of living kidney donations has increased to 275 for the year 2005. This means that in the Netherlands currently over 40% of all kidneys transplanted come from living donors. In the past, only the patients’ close relatives were considered suitable as living donors for immunological reasons, but over the last decades non-related living donor kidneys have proved to give similar good outcomes. In addition, surgical techniques have improved, resulting in better outcomes for living kidney donors. At present the laparoscopic donor nephrectomy is advocated as the preferable surgical approach, because of the beneficial effect on the quality of life and the earlier return to work of the donors. It is partly because of these findings that the practice of living kidney donation has rapidly developed over the past decade.
Effects of High-Pressure Processing on Immunoglobulin A and Lysozyme Activity in Human Milk

Banked human milk, processed using low-temperature/long-time or Holder pasteurization, inactivates pathogenic microorganisms but degrades important biochemical components. High-pressure processing kinetics favor inactivation of microorganisms with retention of biochemical activity and nutritional quality of foods. The effects of high-pressure processing (400 MPa) and low-temperature/long-time pasteurization (62.5°C, 30 minutes) on total immunoglobulin A and lysozyme activity in human milk were investigated. Indirect modified enzyme-linked immunosorbent and a Micrococcus lysodeikticus turbidimetric assay were performed to measure immunoglobulin A immunoactivity and lysozyme activity, respectively. Pressure-treated samples retained significantly higher (P < .05) levels of immunoglobulin A and lysozyme activity compared to samples treated with low-temperature/ long-time pasteurization. These data suggest that high-pressure processing is a potential alternative to thermal pasteurization of human milk that can give greater retention of some bioactive components. Further research is needed to determine whether high-pressure processing can inactivate pathogens of concern in donor human milk. J Hum Lact. 23(3):253-261.
Transient Advantage

Strategy is stuck. For too long the business world has been obsessed with the notion of building a sustainable competitive advantage. That idea is at the core of most strategy textbooks; it forms the basis of Warren Buffett’s investment strategy; it’s central to the success of companies on the “most admired” lists. I’m not arguing that it’s a bad idea—obviously, it’s marvelous to compete in a way that others can’t imitate. And even today there are companies that create a strong position and defend it for extended periods of time—firms such as GE, IKEA, Unilever, Tsingtao Brewery, and Swiss Re. But it’s now rare for a company to maintain a truly lasting advantage. Competitors and customers have become too unpredictable, and industries too amorphous. The forces at work here are familiar: the digital revolution, a “flat” world, fewer barriers to entry, globalization.
Using the Artificial Neural Networks for Accurate RF DevicesModeling

Abstract: In the recent PSpice programs, five types of the GaAs FET model hav been implemented. However, some of them are too sophisticated and therefore difficult to measure and identify afterwards, especially the realistic model of Parker and Skellern. In the paper, simple enhancements of one of the classical models are proposed first. The resulting modification is usable for reli able modeling of both GaAs FETs and pHEMTs. Moreover, its adjusted capacitance function can effective ly s rve as a convenient representation of microwave varactors. The accuracy of these models can be strongly enha nced using the artificial neural networks – both using an exclusive neural network without an analytic model and co-operating a corrective neural network with the updated analytic model are discussed. The accuracy of th e updated analytic models, the models based on the exclusive neural network, and the models created as a com bination of the updated analytic model and the corrective neural network is compared.
Dental Fear of 6-12-year-old Children - Role of Parents, Gender and Age.

AIM To investigate the level of dental fear in middle childhood and the impact of various personal and social factors that contribute to developing dental fear. MATERIALS AND METHODS The study included sixty-seven 6-12-year-old children and their parents, randomly selected while receiving their treatment at the Department of Pediatric Dentistry, Plovdiv. A modified version of Dental Subscale of the Children’s Fear Survey Schedule (CFSS-DS) was used to assess the dental fear of each patient. An interview was conducted with the parents to gather information about patient’s gender and age, parent’s dental anxiety (Corah’s dental anxiety scale-DAS) and pre-appointment preparation of children. RESULTS The results show that the mean of CFSS-DS for children is 30.28±1.17. Concerning the age and gender the comparison between groups of patients with different levels of dental fear demonstrate no significant difference (p>0.05). 60.6%±0.49 of parents prepare their children themselves for the future dental visit and 39.4%±0.49 rely on the dentists to do it. The analysis shows that parent’s dental anxiety and pre-appointment preparation are not associated with the level of dental fear of their children (p>0.05). CONCLUSIONS In middle childhood the majority of children show no or low level of dental fear. The personal and social factors we studied are not determinant risk factors for 6-12-year-old children to develop dental fear.
Effect of motivation strategieson adolescent nursing students' classroom engagement and satisfaction

Background: In order for students to be successful in school, they must be motivated to learn. Teachers are facilitators of knowledge, as well as motivation, and have the ability to lead students toward success. Teachers can motivate through the use of intrinsic motivation and/or extrinsic motivation. Highly motivated students make an effort to be engaged in class. The more students engage themselves in academic activities, the more they will be successful.Purpose:To determine the effect of motivation strategies on adolescent nursing students' classroom engagement and satisfaction level.Design:Pretest and posttest quasi-experimental research design were used in this study. Setting: This study was carried out at Nursing Education Department, Faculty of Nursing-Alexandria University.Subjects:convenient samples of 79 adolescent nursing students(less than 19 years old) enrolled in the" Educational Strategies in Nursing" course were included in the current study. Tools: two tools were used; Adolescent nursing students' classroom engagement scaleand Adolescent nursing students' satisfaction scale.Results: Highly statistically significant differences were found related to the students'engagement before and after the application of motivation strategies. Almost all of them were highly satisfied after the application of motivation strategies.Conclusion:Using motivation strategies in teaching is an effective way for improving the adolescent nursing students' classroom engagement and increasing their satisfaction levels. Recommendation:Educational workshops should be conducted to all nurse educators responsible for classroom teaching to increase their awareness about the motivation strategies and their advantages in enhancing teaching learning process.
Determinants of Capital Structure: An Application on Manufacturing Firms in Borsa Istanbul

This paper attempts to analyze the significant determinants of capital structure in Turkish manufacturing industry using panel data methodology. This study employs a data set containing annual data from 131 Turkish listed companies in Borsa Istanbul between the years 2005 – 2014 to document their capital structure characteristics. Because of many studies that examine capital structure concentrate on nonfinancial companies, we choose manufacturing firms as our sample. Since International Financial Reporting Standarts (IFRS) adoption has started in 2005 for Turkish listed firms, the period of 2005-2014 selected for this paper. In this study, seven firm specific determinants are used as independent variables to analyze capital structure of Turkish firms. These are; Firm Size, Profitability, Growth in total asset, Earnings volatility, Tangibility, Non-debt tax shields and Liquidity. According to results, Firm Size and Non-Debt Tax Shield have positive impacts on Turkish Manufacturing Firms’ financial leverage as expected. In addition, Profitability and Liquidity variables have negative effects on gearing as mentioned in the literature. However, Tangibility and Growth in total asset variables do not influence the leverage as expected in our hypothesis yet they are still significant. There is an ambiguity for these two variables’ effects on leverage in the literature. Lastly Earnings Volatility is the only variable that is insignificant and rejected.
The state of quantitative skills in undergraduate science education: Findings from an Australian study July 2012

The project, QS in Science: Curriculum Models for the Future, responded to the call for action to address the inadequacy of quantitative skills (QS) among science students and graduates. The QS in Science project espouses development of QS as a key learning outcome through embedding QS in undergraduate science programs in a 'whole of program' approach. A year and half into the project, our findings affirm that the challenges faced by the higher education sector regarding QS in science are serious and deep-seated, with profound influence on the program structures of science undergraduate courses. Seven key findings verify that these challenges are substantial.
Product Design Under Multinomial Logit Choices: Optimization of Quality and Prices in an Evolving Product Line

Problem definition: We study a product-line design problem in which customer choice among multiple products is given by a multinomial logit (MNL) model. A firm determines product quality and prices in an evolving product line to maximize profit. In particular, given the prices and quality of products that already exist in a product line, the firm optimizes prices and/or quality of the new products. Academic/practical relevance: We extend the literature on discrete choice models to include the interaction between product quality and product price and consider two variations of the problem, each mirroring a relevant decision setting found in practice: Variation i is a price-optimization problem in which the firm determines prices of the new products given the quality. Variation ii is a joint price and quality optimization of the new products. Methodology: We apply convex optimization techniques and analyze properties of optimal solutions. Results: We establish concavity of the profit function under price optimization and present tractable solution approaches for the joint quality–price optimization. For each problem variation, we characterize the optimal solution and develop efficient algorithms. We show that the interaction of price and quality is central not only to reconciling the divergence of the existing literature’s equal-markup price prediction from differentiated markups observed in practice, but also for explaining differentiated quality measures across products; this empirically observed strategy can now be quantified and optimized with the model developed in this paper. In addition, we show that the presence of existing products tends to drive the firm to offer new products with both higher quality and prices because of the price–quality interaction. Managerial implications: Findings of this paper offer not only managerial guidelines, but also tools for decision support because of the wide empirical applicability of the MNL model. An important managerial implication is that the lack of realism in the linear utility of the MNL model can be addressed by including price–quality interaction, which is central to understanding the quality and price decision in product-line design. The interaction rationalizes the matching of high markup with high quality and justifies differentiated offering of new products in the presence of existing products.