Abstract: This research examined the literature review of wedding preparation’s challenges and its developmental tasks of family transition under family life cycle. Five interviewees were invited to share their experiences on the differences with their parents in regard to wedding preparations and coping strategies. Some coping strategies and processes were highlighted for facilitating the family to achieve the developmental tasks during the wedding preparation. However, those coping strategies and processes may only act as the step and the behavior, while “concern towards parents” was found to be the essential element behind these behaviors. In addition to pre-marital counseling, a developmental group was suggested to develop under the framework of family life cycle and its related coping strategies on working with the newlyweds who encountered intergenerational differences in regard to their wedding preparations.
Abstract: This paper examines how “Zakat” provides fair
income redistribution and aids the struggle against poverty. Providing
fair income redistribution and combating poverty constitutes some of
the fundamental tasks performed by countries all over the world.
Each country seeks a solution for these problems according to their
political, economic and administrative styles through applying
various economic and financial policies. The same situation can be
handled via “zakat” association in Islam. Nowadays, we observe
different versions of “zakat” in developed countries. Applications
such as negative income tax denote merely a different form of
“zakat” that is being applied almost in the same way but under
changed names. However, the minimum values to donate under zakat
(e.g. 85 gr. gold and 40 animals) get altered and various amounts are
put into practice. It might be named as negative income tax instead of
zakat, nonetheless, these applications are based on the Holy Koran
and the hadith released 1400 years ago. Besides, considering the
savage and slavery in the world at those times, we might easily
recognize the true value of the zakat being applied for the first time
then in the Islamic system. Through zakat, governments are able to
transfer incomes to the poor as a means of enabling them achieve the
minimum standard of living required. With regards to who benefits
from the Zakat, an objective and fair criteria was used to determine
who benefits from the zakat contrary to the notion that it was based
on peoples’ own choices. Since the zakat is obligatory, the transfers
do not get forwarded directly but via the government and get
distributed, which requires vast governmental organizations. Through
the application of Zakat, reduced levels of poverty can be achieved
and also ensure the fair income redistribution.
Abstract: TELUM software is a land use model designed specifically to help metropolitan planning organizations (MPOs) prepare their transportation improvement programs and fulfill their numerous planning responsibilities. In this context obtaining, preparing, and validating socioeconomic forecasts are becoming fundamental tasks for an MPO in order to ensure that consistent population and employment data are provided to travel demand models. Chittenden County Metropolitan Planning Organization of Vermont State was used as a case study to test the applicability of TELUM land use model. The technical insights and lessons learned from the land use model application have transferable value for all MPOs faced with land use forecasting development and transportation modeling.
Abstract: Classification of electroencephalogram (EEG) signals
extracted during mental tasks is a technique that is actively pursued
for Brain Computer Interfaces (BCI) designs. In this paper, we
compared the classification performances of univariateautoregressive
(AR) and multivariate autoregressive (MAR) models
for representing EEG signals that were extracted during different
mental tasks. Multilayer Perceptron (MLP) neural network (NN)
trained by the backpropagation (BP) algorithm was used to classify
these features into the different categories representing the mental
tasks. Classification performances were also compared across
different mental task combinations and 2 sets of hidden units (HU): 2
to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different
mental tasks from 4 subjects were used in the experimental study and
combinations of 2 different mental tasks were studied for each
subject. Three different feature extraction methods with 6th order
were used to extract features from these EEG signals: AR
coefficients computed with Burg-s algorithm (ARBG), AR
coefficients computed with stepwise least square algorithm (ARLS)
and MAR coefficients computed with stepwise least square
algorithm. The best results were obtained with 20 to 100 HU using
ARBG. It is concluded that i) it is important to choose the suitable
mental tasks for different individuals for a successful BCI design, ii)
higher HU are more suitable and iii) ARBG is the most suitable
feature extraction method.
Abstract: The objective of this paper is to characterize the spontaneous Electroencephalogram (EEG) signals of four different motor imagery tasks and to show hereby a possible solution for the present binary communication between the brain and a machine ora Brain-Computer Interface (BCI). The processing technique used in this paper was the fractal analysis evaluated by the Critical Exponent Method (CEM). The EEG signal was registered in 5 healthy subjects,sampling 15 measuring channels at 1024 Hz.Each channel was preprocessed by the Laplacian space ltering so as to reduce the space blur and therefore increase the spaceresolution. The EEG of each channel was segmented and its Fractaldimension (FD) calculated. The FD was evaluated in the time interval corresponding to the motor imagery and averaged out for all the subjects (each channel). In order to characterize the FD distribution,the linear regression curves of FD over the electrodes position were applied. The differences FD between the proposed mental tasks are quantied and evaluated for each experimental subject. The obtained results of the proposed method are a substantial fractal dimension in the EEG signal of motor imagery tasks and can be considerably utilized as the multiple-states BCI applications.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification
Abstract: One of the vital developmental tasks that an
individual faces during adolescence is choosing a career. Arriving at
a career decision is difficult and anxious for many adolescents in the
tertiary level. The main purpose of this study is to determine the
factors relating to career indecision among freshmen college students
as basis for the formulation of a comprehensive career counseling
program for the psychological well-being of freshmen university
students. The subjects were purposively selected. The Slovin-s
formula was used in determining the sample size, using a 0.05
margin of error in getting the total number of samples per college and
per major. The researcher made use of descriptive correlational study
in determining significant factors relating to career indecision.
Multiple Regression Analysis indicated that career thoughts, career
decisions and vocational identity as factors related to career
indecision.