Abstract: The aim of this research was to reveal the link
between mental variables, such as spatial abilities, memory, intellect
and professional experience of drivers.
Participants were allocated to four groups: no experience,
inexperienced, skilled and professionals (total 85 participants). The
level of ability for spatial navigation and indicator of nonverbal
memory grow along the process of accumulation of driving
experience. At high levels of driving experience, this tendency is
especially noticeable. The professionals having personal
achievements in driving (racing) differ from skilled drivers in better
feeling of direction, which is specific for them not just in a short-term
situation of an experimental task, but also in life-size perspective.
The level of ability of mental rotation does not grow with the growth
of driving experience, which confirms the multiple intelligence
theory according to which spatial abilities represent specific, other
than logical intelligence type of intellect. The link between spatial
abilities, memory, intellect and professional experience of drivers
seems to be different relating spatial navigation or mental rotation as
different kinds of spatial abilities.
Abstract: A novel physico-chemical route to produce few layer graphene nanoribbons with atomically smooth edges is reported, via acid treatment (H2SO4:HNO3) followed by characteristic thermal shock processes involving extremely cold substances. Samples were studied by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy and X-ray photoelectron spectroscopy. This method demonstrates the importance of having the nanotubes open ended for an efficient uniform unzipping along the nanotube axis. The average dimensions of these nanoribbons are approximately ca. 210 nm wide and consist of few layers, as observed by transmission electron microscopy. The produced nanoribbons exhibit different chiralities, as observed by high resolution transmission electron microscopy. This method is able to provide graphene nanoribbons with atomically smooth edges which could be used in various applications including sensors, gas adsorption materials, composite fillers, among others.
Abstract: This paper presents a mathematical model and a
methodology to analyze the losses in transmission expansion
planning (TEP) under uncertainty in demand. The methodology is
based on discrete particle swarm optimization (DPSO). DPSO is a
useful and powerful stochastic evolutionary algorithm to solve the
large-scale, discrete and nonlinear optimization problems like TEP.
The effectiveness of the proposed idea is tested on an actual
transmission network of the Azerbaijan regional electric company,
Iran. The simulation results show that considering the losses even for
transmission expansion planning of a network with low load growth
is caused that operational costs decreases considerably and the
network satisfies the requirement of delivering electric power more
reliable to load centers.