Distance education programs in higher education are gaining popularity mostly due to the flexibility of the formative programs to fit all the requirements that brick-and-mortar educational institutions are not able to provide to students. However, quite often these distance programs report feelings of isolation, lack of self-direction and management, and eventual decrease in motivation levels. Thus, the main aim of this research is to assess the effect of following an active learning methodology on the students’ emotions, self-efficacy beliefs and learning outcomes in the context of a distance learning program in an Atmospheric Pollution course. According to the results, the use of these methodologies not only had a significant promotion in the positive emotions and self-efficacy beliefs, but also positive impact in the students’ learning outcomes. The results obtained in this research demonstrate that following an appropriate learning methodology in a distance program could contribute to reduce the main handicaps of these programs.
In this paper, a generalization of the concept of electrical power for periodic current and voltage waveforms based on a new generalized complex geometric algebra (GCGA), is proposed. This powerful tool permits, in n-sinusoidal/ nonlinear situations, representing and calculating the voltage, current, and apparent power in a single-port electrical network in terms of multivectors. The new expressions result in a novel representation of the apparent power, similar to the Steinmetz's phasor model, based on complex numbers, but limited to the purely sinusoidal case. The multivectorial approach presented is based on the frequency domain decomposition of the apparent power into three components: the real part and the imaginary part of the complex-scalar associated to active and reactive power respectively, and distortion power, associated to the complex-bivector. A geometrical interpretation of the multivectorial components of apparent power is discussed. Numerical examples illustrate the clear advantages of the suggested approach.
This paper presents an effective approach to identify power quality events based on IEEE Std 1159-2009 caused by intermittent power sources like those of renewable energy. An efficient characterization of these disturbances is granted by the use of two useful wavelet based indices. For this purpose, a wavelet-based Global Disturbance Ratio index (GDR), defined through its instantaneous precursor (Instantaneous Transient Disturbance index ITD(t)), is used in power distribution networks (PDN) under steady-state and/or transient conditions. An intelligent disturbance classification is done using a Support Vector Machine (SVM) with a minimum input vector based on the GDR index. The effectiveness of the proposed technique is validated using a real-time experimental system with single events and multi-events signals.
The estrogenic compound diethylstilbestrol (DES) is widely studied because of its potential endocrine disruption effects. The prohibition of the use of diethylstilbestrol as a growth promoter has not been enough to ensure the total disappearance of this compound from environmental matrices. Due to the low levels of DES present in the environment, preconcentration and clean up methods are necessary for its analysis. This paper describes the synthesis and use of a molecularly imprinted polymer (MIP) as sorbent for on-column solid-phase extraction of DES from aqueous samples. The selectivity of the DES-MIP was evaluated towards several selected estrogens such as hexestrol (HEX), estrone (E1), estriol (E3), estradiol (E2) and ethynylestradiol (EE2). HPLC-DAD was used to quantify all analytes at 230-nm wavelength. The method has been successfully applied to the analysis of DES in spiked river and tap water samples, with recoveries of 72% and 83% respectively.
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