Abstract: The apportionment method is used by many countries, to calculate the distribution of seats in political bodies. For example, this method is used in the United States (U.S.) to distribute house seats proportionally based on the population of the electoral district. Famous apportionment methods include the divisor methods called the Adams Method, Dean Method, Hill Method, Jefferson Method and Webster Method. Sometimes the results from the implementation of these divisor methods are unfair and include errors. Therefore, it is important to examine the optimization of this method by using a bias measurement to figure out precise and fair results. In this research we investigate the bias of divisor methods in the U.S. Houses of Representatives toward large and small states by applying the Stolarsky Mean Method. We compare the bias of the apportionment method by using two famous bias measurements: the Balinski and Young measurement and the Ernst measurement. Both measurements have a formula for large and small states. The Third measurement however, which was created by the researchers, did not factor in the element of large and small states into the formula. All three measurements are compared and the results show that our measurement produces similar results to the other two famous measurements.
Abstract: Two normal populations with different means and same
variance are considered, where the variance is known. The population
with the smaller sample mean is selected. Various estimators are
constructed for the mean of the selected normal population. Finally,
they are compared with respect to the bias and MSE risks by
the mehod of Monte-Carlo simulation and their performances are
analysed with the help of graphs.
Abstract: Objective: Sharing devastating news with patients is
often considered the most difficult task of doctors. This study aimed
to explore patients’ perceptions of receiving bad news including
which features improve the experience and which areas need refining. Methods: A questionnaire was written based on the steps of the
SPIKES model for breaking bad new. 20 patients receiving treatment
for a hematological malignancy completed the questionnaire. Results: Overall, the results are promising as most patients praised
their consultation. ‘Poor’ was more commonly rated by women and
participants aged 45-64. The main differences between the ‘excellent’
and ‘poor’ consultations include the doctor’s sensitivity and checking
the patients’ understanding. Only 35% of patients were asked their
existing knowledge and 85% of consultations failed to discuss the
impact of the diagnosis on daily life. Conclusion: This study agreed with the consensus of existing
literature. The commended aspects include consultation set-up and
information given. Areas patients felt needed improvement include
doctors determining the patient’s existing knowledge and checking
new information has been understood. Doctors should also explore
how the diagnosis will affect the patient’s life. With a poorer
prognosis, doctors should work on conveying appropriate hope. The
study was limited by a small sample size and potential recall bias.
Abstract: In this article, a new method is proposed for the measuring of well-being inequality through a model composed of superimposing satisfaction waves. The displacement of households’ satisfactory state (i.e. satisfaction) is defined in a satisfaction string. The duration of the satisfactory state for a given period is measured in order to determine the relationship between utility and total satisfactory time, itself dependent on the density and tension of each satisfaction string. Thus, individual cardinal total satisfaction values are computed by way of a one-dimensional form for scalar sinusoidal (harmonic) moving wave function, using satisfaction waves with varying amplitudes and frequencies which allow us to measure wellbeing inequality. One advantage to using satisfaction waves is the ability to show that individual utility and consumption amounts would probably not commute; hence, it is impossible to measure or to know simultaneously the values of these observables from the dataset. Thus, we crystallize the problem by using a Heisenberg-type uncertainty resolution for self-adjoint economic operators. We propose to eliminate any estimation bias by correlating the standard deviations of selected economic operators; this is achieved by replacing the aforementioned observed uncertainties with households’ perceived uncertainties (i.e. corrected standard deviations) obtained through the logarithmic psychophysical law proposed by Weber and Fechner.
Abstract: An approach was evaluated for the retrieval of soil
moisture of bare soil surface using bistatic scatterometer data in the
angular range of 200 to 700 at VV- and HH- polarization. The
microwave data was acquired by specially designed X-band (10
GHz) bistatic scatterometer. The linear regression analysis was done
between scattering coefficients and soil moisture content to select the
suitable incidence angle for retrieval of soil moisture content. The 250
incidence angle was found more suitable. The support vector
regression analysis was used to approximate the function described
by the input output relationship between the scattering coefficient and
corresponding measured values of the soil moisture content. The
performance of support vector regression algorithm was evaluated by
comparing the observed and the estimated soil moisture content by
statistical performance indices %Bias, root mean squared error
(RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias,
root mean squared error (RMSE) and Nash-Sutcliffe Efficiency
(NSE) were found 2.9451, 1.0986 and 0.9214 respectively at HHpolarization.
At VV- polarization, the values of %Bias, root mean
squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were
found 3.6186, 0.9373 and 0.9428 respectively.
Abstract: Research Objectives: The roles and activities of
Human Resource Management (HRM) have changed a lot in the past
years. Driven by a changing environment and therefore new business
requirements, the scope of human resource (HR) activities has
widened. The extent to which these activities should focus on
strategic issues to support the long term success of a company has
been discussed in science for many years. As many economies of
Central and Eastern Europe (CEE) experienced a phase of transition
after the socialist era and are now recovering from the 2008 global
crisis it is needed to examine the current state of HR positioning.
Furthermore a trend in HR work developing from rather
administrative units to being strategic partners of management can be
noticed. This leads to the question of better understanding the
underlying competencies which are necessary to support
organisations. This topic was addressed by the international study
“HR Competencies in international comparison”. The quantitative
survey was conducted by the Institute for Human Resources &
Organisation of FHWien University of Applied Science of WKW (A)
in cooperation with partner universities in the countries Bosnia-
Herzegovina, Croatia, Serbia and Slovenia. Methodology: Using the
questionnaire developed by Dave Ulrich we tested whether the HR
Competency model can be used for Austria, Bosnia and Herzegovina,
Croatia, Serbia and Slovenia. After performing confirmatory and
exploratory factor analysis for the whole data set containing all five
countries we could clearly distinguish between four competencies. In
a further step our analysis focused on median and average
comparisons between the HR competency dimensions. Conclusion:
Our literature review, in alignment with other studies, shows a
relatively rapid pace of development of HR Roles and HR
Competencies in BCSS in the past decades. Comparing data from
BCSS and Austria we still can notice that regards strategic orientation
there is a lack in BCSS countries, thus competencies are not as
developed as in Austria. This leads us to the tentative conclusion that
HR has undergone a rapid change but is still in a State of Transition
from being a rather administrative unit to performing the role of a
strategic partner.
Abstract: Experimental investigations of the DC electric field effect on thermal decomposition of biomass, formation of the axial flow of volatiles (CO, H2, CxHy), mixing of volatiles with swirling airflow at low swirl intensity (S ≈ 0.2-0.35), their ignition and on formation of combustion dynamics are carried out with the aim to understand the mechanism of electric field influence on biomass gasification, combustion of volatiles and heat energy production. The DC electric field effect on combustion dynamics was studied by varying the positive bias voltage of the central electrode from 0.6 kV to 3 kV, whereas the ion current was limited to 2 mA. The results of experimental investigations confirm the field-enhanced biomass gasification with enhanced release of volatiles and the development of endothermic processes at the primary stage of thermochemical conversion of biomass determining the field-enhanced heat energy consumption with the correlating decrease of the flame temperature and heat energy production at this stage of flame formation. Further, the field-enhanced radial expansion of the flame reaction zone correlates with a more complete combustion of volatiles increasing the combustion efficiency by 3% and decreasing the mass fraction of CO, H2 and CxHy in the products, whereas by 10% increases the average volume fraction of CO2 and the heat energy production downstream the combustor increases by 5-10%
Abstract: A novel design technique employing CMOS Current
Feedback Operational Amplifier (CFOA) is presented. The feature of
consumption very low power in designing pseudo-OTA is used to
decreasing the total power consumption of the proposed CFOA. This
design approach applies pseudo-OTA as input stage cascaded with
buffer stage. Moreover, the DC input offset voltage and harmonic
distortion (HD) of the proposed CFOA are very low values compared
with the conventional CMOS CFOA due to the symmetrical input
stage. P-Spice simulation results are obtained using 0.18μm MIETEC
CMOS process parameters and supply voltage of ±1.2V, 50μA
biasing current. The p-spice simulation shows excellent improvement
of the proposed CFOA over existing CMOS CFOA. Some of these
performance parameters, for example, are DC gain of 62. dB, openloop
gain bandwidth product of 108 MHz, slew rate (SR+) of
+71.2V/μS, THD of -63dB and DC consumption power (PC) of
2mW.
Abstract: In statistics parameter theory, usually the
parameter estimations have two kinds, one is the least-square
estimation (LSE), and the other is the best linear unbiased
estimation (BLUE). Due to the determining theorem of
minimum variance unbiased estimator (MVUE), the parameter
estimation of BLUE in linear model is most ideal. But since
the calculations are complicated or the covariance is not
given, people are hardly to get the solution. Therefore, people
prefer to use LSE rather than BLUE. And this substitution
will take some losses. To quantize the losses, many scholars
have presented many kinds of different relative efficiencies in
different views. For the linear weighted regression model, this
paper discusses the relative efficiencies of LSE of β to BLUE
of β. It also defines two new relative efficiencies and gives
their lower bounds.
Abstract: This research paper presents highly optimized barrel
shifter at 22nm Hi K metal gate strained Si technology node. This
barrel shifter is having a unique combination of static and dynamic
body bias which gives lowest power delay product. This power delay
product is compared with the same circuit at same technology node
with static forward biasing at ‘supply/2’ and also with normal reverse
substrate biasing and still found to be the lowest. The power delay
product of this barrel sifter is .39362X10-17J and is lowered by
approximately 78% to reference proposed barrel shifter at 32nm bulk
CMOS technology. Power delay product of barrel shifter at 22nm Hi
K Metal gate technology with normal reverse substrate bias is
2.97186933X10-17J and can be compared with this design’s PDP of
.39362X10-17J. This design uses both static and dynamic substrate
biasing and also has approximately 96% lower power delay product
compared to only forward body biased at half of supply voltage. The
NMOS model used are predictive technology models of Arizona state
university and the simulations to be carried out using HSPICE
simulator.
Abstract: The problems arising from unbalanced data sets
generally appear in real world applications. Due to unequal class
distribution, many researchers have found that the performance of
existing classifiers tends to be biased towards the majority class. The
k-nearest neighbors’ nonparametric discriminant analysis is a method
that was proposed for classifying unbalanced classes with good
performance. In this study, the methods of discriminant analysis are
of interest in investigating misclassification error rates for classimbalanced
data of three diabetes risk groups. The purpose of this
study was to compare the classification performance between
parametric discriminant analysis and nonparametric discriminant
analysis in a three-class classification of class-imbalanced data of
diabetes risk groups. Data from a project maintaining healthy
conditions for 599 employees of a government hospital in Bangkok
were obtained for the classification problem. The employees were
divided into three diabetes risk groups: non-risk (90%), risk (5%),
and diabetic (5%). The original data including the variables of
diabetes risk group, age, gender, blood glucose, and BMI were
analyzed and bootstrapped for 50 and 100 samples, 599 observations
per sample, for additional estimation of the misclassification error
rate. Each data set was explored for the departure of multivariate
normality and the equality of covariance matrices of the three risk
groups. Both the original data and the bootstrap samples showed nonnormality
and unequal covariance matrices. The parametric linear
discriminant function, quadratic discriminant function, and the
nonparametric k-nearest neighbors’ discriminant function were
performed over 50 and 100 bootstrap samples and applied to the
original data. Searching the optimal classification rule, the choices of
prior probabilities were set up for both equal proportions (0.33: 0.33:
0.33) and unequal proportions of (0.90:0.05:0.05), (0.80: 0.10: 0.10)
and (0.70, 0.15, 0.15). The results from 50 and 100 bootstrap samples
indicated that the k-nearest neighbors approach when k=3 or k=4 and
the defined prior probabilities of non-risk: risk: diabetic as 0.90:
0.05:0.05 or 0.80:0.10:0.10 gave the smallest error rate of
misclassification. The k-nearest neighbors approach would be
suggested for classifying a three-class-imbalanced data of diabetes
risk groups.
Abstract: In general, classical methods such as maximum
likelihood (ML) and least squares (LS) estimation methods are used
to estimate the shape parameters of the Burr XII distribution.
However, these estimators are very sensitive to the outliers. To
overcome this problem we propose alternative robust estimators
based on the M-estimation method for the shape parameters of the
Burr XII distribution. We provide a small simulation study and a real
data example to illustrate the performance of the proposed estimators
over the ML and the LS estimators. The simulation results show that
the proposed robust estimators generally outperform the classical
estimators in terms of bias and root mean square errors when there
are outliers in data.
Abstract: For optimal unbiased filter as mean-square and in the
case of functioning anomalous noises in the observation memory
channel, we have proved insensitivity of filter to inaccurate
knowledge of the anomalous noise intensity matrix and its
equivalence to truncated filter plotted only by non anomalous
components of an observation vector.
Abstract: In urban context, urban nodes such as amenity or
hazard will certainly affect house price, while classic hedonic analysis
will employ distance variables measured from each urban nodes.
However, effects from distances to facilities on house prices generally
do not represent the true price of the property. Distance variables
measured on the same surface are suffering a problem called
multicollinearity, which is usually presented as magnitude variance
and mean value in regression, errors caused by instability. In this paper,
we provided a theoretical framework to identify and gather the data
with less bias, and also provided specific sampling method on locating
the sample region to avoid the spatial multicollinerity problem in three
distance variable’s case.
Abstract: Stratified double extreme ranked set sampling
(SDERSS) method is introduced and considered for estimating the
population mean. The SDERSS is compared with the simple random
sampling (SRS), stratified ranked set sampling (SRSS) and stratified
simple set sampling (SSRS). It is shown that the SDERSS estimator
is an unbiased of the population mean and more efficient than the
estimators using SRS, SRSS and SSRS when the underlying
distribution of the variable of interest is symmetric or asymmetric.
Abstract: In urban area, several landmarks may affect housing
price and rents, and hedonic analysis should employ distance variables
corresponding to each landmarks. Unfortunately, the effects of
distances to landmarks on housing prices are generally not consistent
with the true price. These distance variables may cause magnitude
error in regression, pointing a problem of spatial multicollinearity. In
this paper, we provided some approaches for getting the samples with
less bias and method on locating the specific sampling area to avoid
the multicollinerity problem in two specific landmarks case.
Abstract: This paper discusses the undesirable charge transfer
through the parasitic capacitances of the input transistors in a
multi-inputs voltage sense amplifier. Its intrinsic rail-to-rail voltage
transitions at the output nodes inevitably disturb the input sides
through the capacitive coupling between the outputs and inputs. Then,
it can possible degrade the stabilities of the reference voltage levels.
Moreover, it becomes more serious in multi-channel systems by
altering them for other channels, and so degrades the linearity of the
overall systems. In order to alleviate the internal node voltage
transition, the internal node stabilization techniques are proposed. It
achieves 45% and 40% improvements for node stabilization and input
referred disturbance, respectively.
Abstract: This paper focuses on the assessment of the air
pollution and morbidity relationship in Tunisia. Air pollution is
measured by ozone air concentration and the morbidity is measured
by the number of respiratory-related restricted activity days during
the 2-week period prior to the interview. Socioeconomic data are also
collected in order to adjust for any confounding covariates. Our
sample is composed by 407 Tunisian respondents; 44.7% are women,
the average age is 35.2, near 69% are living in a house built after
1980, and 27.8% have reported at least one day of respiratory-related
restricted activity. The model consists on the regression of the
number of respiratory-related restricted activity days on the air
quality measure and the socioeconomic covariates. In order to correct
for zero-inflation and heterogeneity, we estimate several models
(Poisson, negative binomial, zero inflated Poisson, Poisson hurdle,
negative binomial hurdle and finite mixture Poisson models).
Bootstrapping and post-stratification techniques are used in order to
correct for any sample bias. According to the Akaike information
criteria, the hurdle negative binomial model has the greatest goodness
of fit. The main result indicates that, after adjusting for
socioeconomic data, the ozone concentration increases the probability
of positive number of restricted activity days.
Abstract: Developing young people’s employability is a key
policy issue for ensuring their successful transition to the labour
market and their access to career oriented employment. The youths of
today irrespective of their gender need to acquire the knowledge,
skills and attitudes that will enable them to create or find jobs as well
as cope with unpredictable labour market changes throughout their
working lives. In a study carried out to determine the influence of
gender on job-competencies requirements of chemical-based
industries and undergraduate-competencies acquisition by chemists
working in the industries, all chemistry graduates working in twenty
(20) chemical-based industries that were randomly selected from six
sectors of chemical-based industries in Lagos and Ogun States of
Nigeria were administered with Job-competencies required and
undergraduate-competencies acquired assessment questionnaire. The
data were analysed using means and independent sample t-test. The
findings revealed that the population of female chemists working in
chemical-based industries is low compared with the number of male
chemists; furthermore, job-competencies requirements are found not
to be gender biased while there is no significant difference in
undergraduate-competencies acquisition of male and female
chemists. This suggests that females should be given the same
opportunity of employment in chemical-based industries as their male
counterparts. The study also revealed the level of acquisition of
undergraduate competencies as related to the needs of chemicalbased
industries.
Abstract: Nowadays social media information, such as news,
links, images, or VDOs, is shared extensively. However, the
effectiveness of disseminating information through social media
lacks in quality: less fact checking, more biases, and several rumors.
Many researchers have investigated about credibility on Twitter, but
there is no the research report about credibility information on
Facebook. This paper proposes features for measuring credibility on
Facebook information. We developed the system for credibility on
Facebook. First, we have developed FB credibility evaluator for
measuring credibility of each post by manual human’s labelling. We
then collected the training data for creating a model using Support
Vector Machine (SVM). Secondly, we developed a chrome extension
of FB credibility for Facebook users to evaluate the credibility of
each post. Based on the usage analysis of our FB credibility chrome
extension, about 81% of users’ responses agree with suggested
credibility automatically computed by the proposed system.