Abstract: Intellectual capital reporting becomes critical at
universities, mainly due to the fact that knowledge is the main output
as well as input in these institutions. In addition, universities have
continuous external demands for greater information and
transparency about the use of public funds, and are increasingly
provided with greater autonomy regarding their organization,
management, and budget allocation. This situation requires new
management and reporting systems. The purpose of the present study
is to provide a model for intellectual capital report in Spanish
universities. To this end, a questionnaire was sent to every member of
the Social Councils of Spanish public universities in order to identify
which intangible elements university stakeholders demand most. Our
proposal for an intellectual capital report aims to act as a guide to
help the Spanish universities on the road to the presentation of
information on intellectual capital which can assist stakeholders to
make the right decisions.
Abstract: This paper proposed a novel model for short term load
forecast (STLF) in the electricity market. The prior electricity
demand data are treated as time series. The model is composed of
several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program
written with MATLAB. The load data are treated as time series data.
They are decomposed into several wavelet coefficient series using
the wavelet transform technique known as Non-decimated Wavelet
Transform (NWT). The reason for using this technique is the belief
in the possibility of extracting hidden patterns from the time series
data. The wavelet coefficient series are used to train the neural
networks (NNs) and used as the inputs to the NNs for electricity load
prediction. The Scale Conjugate Gradient (SCG) algorithm is used as
the learning algorithm for the NNs. To get the final forecast data, the
outputs from the NNs are recombined using the same wavelet
technique. The model was evaluated with the electricity load data of
Electronic Engineering Department in Mandalay Technological
University in Myanmar. The simulation results showed that the
model was capable of producing a reasonable forecasting accuracy in
STLF.
Abstract: The complexity of teaching English in higher
institutions by non-native speakers within a second/foreign language
setting has created continuous discussions and research about
teaching approaches and teaching practises, professional identities
and challenges. In addition, there is a growing awareness that
teaching English within discipline-specific contexts adds up to the
existing complexity. This awareness leads to reassessments,
discussions and suggestions on course design and content and
teaching approaches and techniques. In meeting expectations
teaching at a university specified in a particular discipline such as
engineering, English language educators are not only required to
teach students to be able to communicate in English effectively but
also to teach soft skills such as problem solving skills. This paper is
part of a research conducted to investigate how English language
educators negotiate with the complexities of teaching problem
solving skills through English language teaching at a technical
university. This paper reports the way an English language educator
identified himself and the way he approached his teaching in this
institutional context.