Miracle-dz/UniFAR-FTUs huggingface dataset:
MANIFESTATION OF THE TEACHER’S REFLECTIVE PRACTICE AS EXPERIENTIAL LEARNING AND INVESTIGATION OF ONE’S ACTIONS IN PROFESSIONAL ACTIVITIES

The article deals with the teacher’s reflective practice as manifestation of learning from experience and investigation of one’s actions in professional activities, presenting the results of the quantitative empirical study. The conducted exploratory factor analysis, applying rotation of factor axes by Varimax method enabled to identify significant factors of the teacher’s reflective practice. Four statistically significant factors were distinguished: investigation of professional and personal activities as a guarantee of accumulation and improvement of professional experience; analysis and refection on personal experience as a factor motivating learning; dissemination of acquired experience in the interaction with school community members as a possibility to learn; learning from the experience as a precondition for success of the learning organization. Research results revealed that teachers understand the importance of learning from experience; however, they are not inclined to conduct a deeper analysis of their practice.
Capital Structure as a Strategic Variable: Evidence from Collective Bargaining

I analyze the strategic use of debt financing to improve a firm's bargaining position with an important supplier-organized labor. Because maintaining high levels of corporate liquidity can encourage workers to raise their wage demands, a firm with external finance constraints has an incentive to use the cash flow demands of debt service to improve its bargaining position with workers. Using both firm-level collective bargaining coverage and state changes in labor laws to identify changes in union bargaining power, I show that strategic incentives from union bargaining appear to have a substantial impact on corporate financing decisions. Copyright (c) 2010 The American Finance Association.
Optimal Integration of Plug-In Hybrid Electric Vehicles in Microgrids

Plug-in hybrid electric vehicles' (PHEVs) integration can result in additional electricity consumers and electricity suppliers in microgrids (MGs). This paper presents a new method for optimal integration of PHEVs in MGs, which considers the optimal number of parking numbers under optimal scheduling of PHEVs. Due to the uncertainty of solar energy, the radial basis function network (RBFN) techniques are used for forecasting photovoltaic (PV) power output. Monte Carlo simulation is used to deal with the uncertainties associated with the daily distance driven of the PHEVs, load values, and electricity market price. The objective is the minimization of total cost (TC) and optimal set of the optimization problem is found using genetic algorithm (GA) method. In order to verify the effectiveness of the proposed method, two market policies are used as case studies. The computation results can be used to evaluate the impact of PHEVs' integration on economical performance of MGs.
Differential Effects of Nicotine Treatment and Ethanol Self-Administration on CYP2A6, CYP2B6 and Nicotine Pharmacokinetics in African Green Monkeys

In primates, nicotine is metabolically inactivated in the liver by CYP2A6 and possibly CYP2B6. Changes in the levels of these two enzymes may affect nicotine pharmacokinetics and influence smoking behaviors. This study investigated the independent and combined effects of ethanol self-administration and nicotine treatment (0.5 mg/kg b.i.d. s.c.) on hepatic CYP2A6 and CYP2B6 levels (mRNA, protein, and enzymatic activity), in vitro nicotine metabolism, and in vivo nicotine pharmacokinetics in monkeys. CYP2A6 mRNA and protein levels and in vitro coumarin (selective CYP2A6 substrate) and nicotine metabolism were decreased by nicotine treatment but unaffected by ethanol. CYP2B6 protein levels and in vitro bupropion (selective CYP2B6 substrate) metabolism were increased by ethanol but unaffected by nicotine treatment; CYP2B6 mRNA levels were unaltered by either treatment. Combined ethanol and nicotine exposure decreased CYP2A6 mRNA and protein levels, as well as in vitro coumarin and nicotine metabolism, and increased CYP2B6 protein levels and in vitro bupropion metabolism, with no change in CYP2B6 mRNA levels. Chronic nicotine resulted in higher nicotine plasma levels achieved after nicotine administration, consistent with decreased CYP2A6. Ethanol alone, or combined with nicotine, resulted in lower nicotine plasma levels by a mechanism independent of the change in these enzymes. Thus, nicotine can decrease hepatic CYP2A6, reducing the metabolism of its substrates, including nicotine, whereas ethanol can increase hepatic CYP2B6, increasing the metabolism of CYP2B6 substrates. In vivo nicotine pharmacokinetics are differentially affected by ethanol and nicotine, but when both drugs are used in combination the effect more closely resembles ethanol alone.
A Qualitative Exploration of Mothers Who Reject Playgroup

ABSTRACT Becoming a mother is a major life experience eliciting a range of physical, psychological, and social changes, resulting in high levels of stress and an increased need for support. Research suggests that mothers’ participation in a community playgroup can facilitate social support, increase parenting confidence, and effectively reduce maternal stress. However, there is currently insufficient research on community playgroups in Australia, particularly in relation to why some mothers reject playgroup. Semi-structured in-depth interviews were conducted with 10 mothers residing in Perth, Western Australia. Using an interpretative phenomenological approach, four main themes were identified: the presence of support; perception of playgroup; identity as a mother; and other sources of information. Understanding the reasons why mothers reject playgroup, and how they experience parenting and social support outside a playgroup setting, provides a vital perspective on the ways in which service providers can improve parent engagement in the program. Further research is required to assess gaps in parental education and awareness of playgroup to improve service delivery.
Libor and Swap Market Models for the Pricing of Interest Rate Derivatives: An Empirical Analysis

In this paper we empirically analyze and compare the Libor and Swap Market Models, developed by Brace, Gatarek, and Musiela (1997) and Jamshidian (1997), using paneldata on prices of US caplets and swaptions.A Libor Market Model can directly be calibrated to observed prices of caplets, whereas a Swap Market Model is calibrated to a certain set of swaption prices.For both one-factor and two-factor models we analyze how well they price caplets and swaptions that were not used for calibration.We show that the Libor Market Models in general lead to better prediction of derivative prices that were not used for calibration than the Swap Market Models.A one-factor Libor Market Model that exhibits mean-reversion gives a good fit of the derivative prices, and adding a second factor only decreases pricing errors to a small extent.We also find that models that are chosen to exactly match certain derivative prices are overfitted. Finally, a regression analysis reveals that the pricing errors are correlated with the shape of the term structure of interest rates.
Over-constrained Weierstrass iteration and the nearest consistent system

We propose a generalization of the Weierstrass iteration for over-constrained systems of equations and we prove that the proposed method is the Gauss-Newton iteration to find the nearest system which has at least $k$ common roots and which is obtained via a perturbation of prescribed structure. In the univariate case we show the connection of our method to the optimization problem formulated by Karmarkar and Lakshman for the nearest GCD. In the multivariate case we generalize the expressions of Karmarkar and Lakshman, and give explicitly several iteration functions to compute the optimum. The arithmetic complexity of the iterations is detailed.
Análise da ansiedade pré­competitiva e competitiva de jovens judocas

Aim: The present study aimed to verify levels of trait anxiety in juvenile judo athletes (between 14 and 18 yrs.) in pre‐competitive and competitive moments. Method: Therefore, 40 athletes (20 boys and 20 girls) with 5 ± 2 yrs. of judo practice were analyzed. The instrument used was the "Sport Competition Anxiety Test” (SCAT). In statistical analysis, we observed differences with Friedman and Mann‐Whitney tests, p ≤ 0.05. The Spearman correlation coefficient was used to identify the relationship between variables. Results: Results demonstrated that males and females presented higher anxiety in championship, when compared with the training period. When compared gender in interaction with the moment, the competitive female group demonstrated higher anxiety than other groups. A significant and negatives correlations between anxiety, during championship and pre‐ competitive moments, and judo practice time were found. Conclusions: These data suggest preparatory work during the championship day should be different for male and female athletes, while female athletes have to
The Empathy Paradox: Increasing Disconnection in the Age of Increasing Connection

The purpose of this chapter is to summarize changes in personality traits that have co-occurred with the rise of new social media, and to evaluate the plausibility of the hypothesis that new social media are a partial explanation for these dramatic changes. Studies have found a rise in social disconnection among recent generations of young Americans. Self-esteem and narcissism have been rising in college students from the late 1970s to 2010, with simultaneous declines in empathy. Scholars and lay people alike blame the rise of the internet, and in particular, self-oriented and self-promoting “social” networking sites. This new media landscape could lead to increasing social disconnection even as it superficially increases our social connections, and several studies suggest a direct link between social media use and social disconnection. However, since most research thus far is correlational, interpretations are limited, leaving open more optimistic possibilities for new social media.
Compensatory mechanisms in Parkinson's disease

Parkinson’s disease (PD) is a common movement disorder, affecting 1% of the population over the age of 65. Pathologically, PD results from degeneration of nigral dopaminergic neurons, however symptoms do not appear until an estimated 50% of these cells are lost, suggesting compensatory mechanisms exist which mask disease onset, and may later delay progression of the disease. Compensation may take place over various spatial and temporal scales, from changes in synaptic dopamine release and synthesis that take place over a period of minutes, to recruitment of novel, widespread networks of brain regions for a specific task, which may require formation of new connections over an extended period of time. Neuroimaging techniques have recently allowed the investigation of regional and network changes in activation related to motor performance in PD, however the question of whether such changes represent a downstream effect of basal ganglia degeneration, or a compensatory change, remains difficult to determine. Here, we applied an approach from research into Alzheimer’s Disease, where abnormal activation patterns are studied in the context of tasks of increasing difficulty, such that inferences regarding their compensatory nature can be made. We show that individuals with PD are able to increase the recruitment of normal networks for a motor task (motor reserve) as a form of compensation, in addition to compensatory recruitment of novel networks to accomplish the same task as healthy controls. In particular, we observe a switch from striato-thalamo-cortical (STC) motor loops to cerebello-thalamo-cortical (CTC) loops as a compensatory strategy. This compensatory recruitment involves changes in the amplitude, spatial extent, and connectivity of regions within the CTC pathway. However, this compensation does not come without a price, since we show that compensatory CTC recruitment involving disconnection between the STC and CTC loops occurs in subjects with tremor-dominant PD, but not akinetic-rigidity-dominant
Dust formation above cool magnetic spots in evolved stars

ABSTRA C T We examine the structure of cool magnetic spots in the photospheres of evolved stars, specifically asymptotic giant branch (AGB) stars and R Coronae Borealis (RCB) stars. We find that the photosphere of a cool magnetic spot will be above the surrounding photosphere of AGB stars, which is the opposite of the situation in the Sun. This results from the behaviour of the opacity, which increases with decreasing temperature, which again is the opposite of the behaviour of the opacity near the effective temperature of the Sun. We analyse the formation of dust above the cool magnetic spots, and suggest that the dust formation is facilitated by strong shocks, driven by stellar pulsations, which run through and around the spots. The presence of both the magnetic field and cooler temperatures makes dust formation easier as the shock passes above the spot. We review some observations supporting the proposed mechanism, and suggest further observations to check the model.
k-Nearest Neighbour Classifiers

Abstract. Perhaps the most straightforward classiﬁer in the arsenal ormachine learning techniques is the Nearest Neighbour Classiﬁer – clas-siﬁcation is achieved by identifying the nearest neighbours to a queryexample and using those neighbours to determine the class of the query.This approach to classiﬁcation is of particular importance today becauseissues of poor run-time performance is not such a problem these dayswith the computational power that is available. This paper presents anoverview of techniques for Nearest Neighbour classiﬁcation focusing on;mechanisms for assessing similarity (distance), computational issues inidentifying nearest neighbours and mechanisms for reducing the dimen-sion of the data. 1 Introduction The intuition underlying Nearest Neighbour Classiﬁcation is quite straightfor-ward, examples are classiﬁed based on the class of their nearest neighbours. Itis often useful to take more than one neighbour into account so the technique ismore commonly referred to as k-Nearest Neighbour (k-NN) Classiﬁcation wherek nearest neighbours are used in determining the class. Since the training ex-amples are needed at run-time, i.e. they need to be in memory at run-time,it is sometimes also called Memory-Based Classiﬁcation. Because induction isdelayed to run time, it is considered a Lazy Learning technique. Because classiﬁ-cation is based directly on the training examples it is also called Example-BasedClassiﬁcation or Case-Based Classiﬁcation. The basic idea is as shown in Figure 1 which depicts a 3-Nearest NeighbourClassiﬁer on a two-class problem in a two-dimensional feature space. In thisexample the decision for q
Their pain is not our pain: Brain and autonomic correlates of empathic resonance with the pain of same and different race individuals

Recent advances in social neuroscience research have unveiled the neurophysiological correlates of race and intergroup processing. However, little is known about the neural mechanisms underlying intergroup empathy. Combining event‐related fMRI with measurements of pupil dilation as an index of autonomic reactivity, we explored how race and group membership affect empathy‐related responses. White and Black subjects were presented with video clips depicting white, black, and unfamiliar violet‐skinned hands being either painfully penetrated by a syringe or being touched by a Q‐tip. Both hemodynamic activity within areas known to be involved in the processing of first and third‐person emotional experiences of pain, i.e., bilateral anterior insula, and autonomic reactivity were greater for the pain experienced by own‐race compared to that of other‐race and violet models. Interestingly, greater implicit racial bias predicted increased activity within the left anterior insula during the observation of own‐race pain relative to other‐race pain. Our findings highlight the close link between group‐based segregation and empathic processing. Moreover, they demonstrate the relative influence of culturally acquired implicit attitudes and perceived similarity/familiarity with the target in shaping emotional responses to others' physical pain. Hum Brain Mapp 34:3168–3181, 2013. © 2012 Wiley Periodicals, Inc.
Dominance Effects of Deleterious and Beneficial Mutations in a Single Gene of the RNA Virus ϕ6

Most of our knowledge of dominance stems from studies of deleterious mutations. From these studies we know that most deleterious mutations are recessive, and that this recessivity arises from a hyperbolic relationship between protein function (i.e., protein concentration or activity) and fitness. Here we investigate whether this knowledge can be used to make predictions about the dominance of beneficial and deleterious mutations in a single gene. We employed a model system – the bacteriophage φ6 – that allowed us to generate a collection of mutations in haploid conditions so that it was not biased toward either dominant beneficial or recessive deleterious mutations. Screening for the ability to infect a bacterial host that does not permit infection by the wildtype φ6, we generated a collection of mutations in P3, a gene involved in attachment to the host and in phage particle assembly. The resulting collection contained mutations with both deleterious and beneficial effects on fitness. The deleterious mutations in our collection had additive effects on fitness and the beneficial mutations were recessive. Neither of these observations were predicted from previous studies of dominance. This pattern is not consistent with the hyperbolic (diminishing returns) relationship between protein function and fitness that is characteristic of enzymatic genes, but could have resulted from a curve of increasing returns.
GABAergic Inhibition in Nucleus Magnocellularis: Implications for Phase Locking in the Avian Auditory Brainstem

In the avian auditory brainstem, nucleus magnocellularis (NM) functions to relay phase-locked signals to nucleus laminaris for binaural coincidence detection. Although many studies have revealed that NM neurons exhibit intrinsic physiological and anatomical specializations for this purpose, the role of inhibition has not been fully explored. The present study characterizes the organization of GABAergic feedback to NM. Anterograde and retrograde labeling methods showed that NM receives a prominent projection from the ipsilateral superior olivary nucleus (SON). The functional features of this projection were explored in a brain slice preparation. Stimulating fibers from the SON evoked long-lasting, depolarizing responses in NM neurons that were blockable by bicuculline, a GABAAreceptor antagonist. The slow time course of these responses allowed them to undergo temporal summation during repetitive stimulation. The summed GABAergic response was capable of blocking spikes generated in NM neurons by suprathreshold current injection. This inhibitory effect was attributable to a large reduction in input resistance caused by a combination of the opening of a GABAergic Cl−conductance and the recruitment of a low-voltage activated K+ conductance. This large reduction of input resistance increased the amount of current necessary to drive NM neurons to threshold. The results lead us to propose that GABAergic inhibition enhances phase-locking fidelity of NM neurons, which is essential to binaural coincidence detection in nucleus laminaris.
Selection and aggregation of interestingness measures: A review

Association Rule Mining is the process of retrieving frequent patterns that occur in a transaction database. Initially used as a market basket analysis solution for retail businesses, it has grown to cover many other fields such as medicine [1, 2], traffic estimation [3] and anomaly detection [4, 5]. An association rule has two components (antecedent and consequent) which is derived from a pattern (a set of items). However, it is not clear when investigating a frequent item set, which items imply the others (i.e., which is antecedent, and which is consequent). Therefore, several combinations of items as antecedent and consequent are generated. This leads to a huge amount of association rules being output by an algorithm for Association Rule Mining. Thus, it is imperative that data miners require some type of measures to evaluate the "interestingness" of these rules. There exist in excess of 70 well-known measures and countless other manually crafted measures in the literature. In this survey, we systematically discuss the methods which users could use to select or aggregate the interestingness measures, applicability of such methods and evaluation of the usage of such methods.
14 Characterization and Application of Thermoelectric Nanowires

In recent years, numerous studies on the electrical transport properties of semiconducting and metallic nanostructures have been performed because of their possible applications in future miniaturized devices, such as thermoelectric sensors or generators. In particular, the electrical transport properties of quasi one-dimensional structures such as nanowires were investigated intensively (Dresselhaus et al., 2003; Cornelius et al., 2006; ToimilMolares et al., 2003). When the structure size is comparable to the electronic mean free path, finite-size effects and quantum-size effects are expected to occur (Fuchs, 1937; Sondheimer, 1952; Mayadas & Shatzkes, 1970). These effects are anticipated to affect also the thermal transport mechanisms. Besides a decrease of the thermal conductivity λ, theories predict an increase of the Seebeck coefficient S and the thermoelectric efficiency z = S2σ /λ, where σ denotes the electrical conductivity, when reducing the dimensionality of the specimen (e.g. by decreasing the diameter of nanowires) (Lin et al., 2000; Zou & Balandin, 2001; Dresselhaus & Heremans, 2006). Therefore, nanowires are promising candidates for new thermoelectric devices (TED) with significantly increased thermoelectric figure of merit. Experimental verification of these predicted effects requires the simultaneous measurement of the above mentioned transport coefficients (S, σ, λ) on single nanowires. In contrast to the extensive studies on electrical transport, only few experimental investigations of thermal conductivity and Seebeck coefficient of nanowires have been reported. For thermal conductivity experiments any thermal bypass has to be avoided, i.e., these studies must not be performed on single wires lying on a substrate but on individual suspended needles. To measure the Seebeck effect accurately, a temperature difference along the nanowire axis (between the electrical contacts) of a few Kelvin has to be established. In the case of a single nanowire contacted between two electrodes placed 10 μm apart, the temperature gradient along the wire amounts 105 K/m. Furthermore, the thermoelectric voltage U caused by this temperature gradient and the temperatures at the nanowire contacts (i.e. at two positions separated by only few micron) have to be measured precisely. Thus, sophisticated microstructured devices are required for the determination of the thermoelectric transport coefficients of individual nanostructures (Shi et al., 2003; Boukai et al., 2006). Measurements of S, σ and λ have been performed on assemblies of many identical nanowires of bismuth