Abstract: The problematic of gender and socioeconomic status
biased differences in academic motivation patterns is discussed.
Gender identity is understood according to symbolic interactionism
perspective: as a result of reflected appraisals, social comparisons,
self-attributions, and identifications, shaped by social environment
and family context. The effects of socioeconomic status on academic
motivation are conceptualized according to Bourdieu’s habitus
concept, reflecting the role of unconscious and internalized cultural
signals, proper to low and high socioeconomic status family contexts.
Since families differ by various socioeconomic features, the
hypothesis about possible impact of parents’ socioeconomic status on
their children’s academic motivation interfering with gender
socialization effects is held. The survey, aiming to seize gender
differences in academic motivation and self-recorded improvementoriented
efforts as a result of socialization processes operating in the
families of low and high socioeconomic status, was designed. The
results of Lithuanian higher education students’ survey are presented
and discussed.
Abstract: In this paper a new design of a broadband microwave
power limiter is presented and validated into simulation by using
ADS software (Advanced Design System) from Agilent technologies.
The final circuit is built on microstrip lines by using identical Zero
Bias Schottky diodes. The power limiter is designed by Associating 3
stages Schottky diodes. The obtained simulation results permit to
validate this circuit with a threshold input power level of 0 dBm until
a maximum input power of 30 dBm.
Abstract: A new relative efficiency in linear model in reference is
instructed into the linear weighted regression, and its upper and lower
bound are proposed. In the linear weighted regression model, for the
best linear unbiased estimation of mean matrix respect to the
least-squares estimation, two new relative efficiencies are given, and
their upper and lower bounds are also studied.
Abstract: Hydrogenated amorphous carbon (a-C:H) films have
been synthesized by a radio frequency plasma enhanced chemical
vapor deposition (rf-PECVD) technique with different bias voltage
from 0.0 to -0.5 kV. The Raman spectra displayed the polymer-like
hydrogenated amorphous carbon (PLCH) film with 0.0 to -0.1 and
a-C:H films with -0.2 to -0.5 kV of bias voltages. The surface chemical
information of all films were studied by X-ray photoelectron
spectroscopy (XPS) technique, presented to C-C (sp2 and sp3) and C-O
bonds, and relative carbon (C) and oxygen (O) atomics contents. The
O contamination had affected on structure and optical properties. The
true density of PLCH and a-C:H films were characterized by X-ray
refractivity (XRR) method, showed the result as in the range of
1.16-1.73 g/cm3 that depending on an increasing of bias voltage. The
hardness was proportional to the true density of films. In addition, the
optical properties i.e. refractive index (n) and extinction coefficient (k)
of these films were determined by a spectroscopic ellipsometry (SE)
method that give formation to in 1.62-2.10 (n) and 0.04-0.15 (k)
respectively. These results indicated that the optical properties
confirmed the Raman results as presenting the structure changed with
applied bias voltage increased.
Abstract: In this paper a scheme is proposed for generating
a programmable current reference which can be implemented
in the CMOS technology. The current can be varied over a
wide range by changing an external voltage applied to one
of the control gates of FGMOS (Floating Gate MOSFET).
For a range of supply voltages and temperature, CMOS
current reference is found to be dependent, this dependence
is compensated by subtracting two current outputs with the
same dependencies on the supply voltage and temperature.
The system performance is found to improve with the
use of FGMOS. Mathematical analysis of the proposed
circuit is done to establish supply voltage and temperature
independence. Simulation and performance evaluation of the
proposed current reference circuit is done using TANNER
EDA Tools. The current reference shows the supply and
temperature dependencies of 520 ppm/V and 312 ppm/oC,
respectively. The proposed current reference can operate down
to 0.9 V supply.
Abstract: Anultra-low power capacitor less low-dropout voltage
regulator with improved transient response using gain enhanced feed
forward path compensation is presented in this paper. It is based on a
cascade of a voltage amplifier and a transconductor stage in the feed
forward path with regular error amplifier to form a composite gainenhanced
feed forward stage. It broadens the gain bandwidth and thus
improves the transient response without substantial increase in power
consumption. The proposed LDO, designed for a maximum output
current of 100 mA in UMC 180 nm, requires a quiescent current of
69 )A. An undershot of 153.79mV for a load current changes from
0mA to 100mA and an overshoot of 196.24mV for current change of
100mA to 0mA. The settling time is approximately 1.1 )s for the
output voltage undershooting case. The load regulation is of 2.77
)V/mA at load current of 100mA. Reference voltage is generated by
using an accurate band gap reference circuit of 0.8V.The costly
features of SOC such as total chip area and power consumption is
drastically reduced by the use of only a total compensation
capacitance of 6pF while consuming power consumption of 0.096
mW.
Abstract: Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.
Abstract: A meta-analysis may be performed using aggregate data (AD) or an individual patient data (IPD). In practice, studies may be available at both IPD and AD level. In this situation, both the IPD and AD should be utilised in order to maximize the available information. Statistical advantages of combining the studies from different level have not been fully explored. This study aims to quantify the statistical benefits of including available IPD when conducting a conventional summary-level meta-analysis. Simulated meta-analysis were used to assess the influence of the levels of data on overall meta-analysis estimates based on IPD-only, AD-only and the combination of IPD and AD (mixed data, MD), under different study scenario. The percentage relative bias (PRB), root mean-square-error (RMSE) and coverage probability were used to assess the efficiency of the overall estimates. The results demonstrate that available IPD should always be included in a conventional meta-analysis using summary level data as they would significantly increased the accuracy of the estimates.On the other hand, if more than 80% of the available data are at IPD level, including the AD does not provide significant differences in terms of accuracy of the estimates. Additionally, combining the IPD and AD has moderating effects on the biasness of the estimates of the treatment effects as the IPD tends to overestimate the treatment effects, while the AD has the tendency to produce underestimated effect estimates. These results may provide some guide in deciding if significant benefit is gained by pooling the two levels of data when conducting meta-analysis.
Abstract: The application of today's semiconductor transistors in high power UHF DVB-T linear amplifiers has evolved significantly by utilizing LDMOS technology. This fact provides engineers with the option to design a single transistor signal amplifier which enables output power and linearity that was unobtainable previously using bipolar junction transistors or later type first generation MOSFETS. The quiescent current stability in terms of thermal variations of the LDMOS guarantees a robust operation in any topology of DVB-T signal amplifiers. Otherwise, progressively uncontrolled heat dissipation enhancement on the LDMOS case can degrade the amplifier’s crucial parameters in regards to the gain, linearity and RF stability, resulting in dysfunctional operation or a total destruction of the unit. This paper presents one more sophisticated approach from the traditional biasing circuits used so far in LDMOS DVB-T amplifiers. It utilizes a microprocessor control technology, providing stability in topologies where IDQ must be perfectly accurate.
Abstract: Temperature rising is a negative factor in almost all systems. It could cause by self heating or ambient temperature. In solar photovoltaic cells this temperature rising affects on the behavior of cells. The ability of a PV module to withstand the effects of periodic hot-spot heating that occurs when cells are operated under reverse biased conditions is closely related to the properties of the cell semi-conductor material.
In addition, the thermal effect also influences the estimation of the maximum power point (MPP) and electrical parameters for the PV modules, such as maximum output power, maximum conversion efficiency, internal efficiency, reliability, and lifetime. The cells junction temperature is a critical parameter that significantly affects the electrical characteristics of PV modules. For practical applications of PV modules, it is very important to accurately estimate the junction temperature of PV modules and analyze the thermal characteristics of the PV modules. Once the temperature variation is taken into account, we can then acquire a more accurate MPP for the PV modules, and the maximum utilization efficiency of the PV modules can also be further achieved.
In this paper, the three-Dimensional Transmission Line Matrix (3D-TLM) method was used to map the surface temperature distribution of solar cells while in the reverse bias mode. It was observed that some cells exhibited an inhomogeneity of the surface temperature resulting in localized heating (hot-spot). This hot-spot heating causes irreversible destruction of the solar cell structure. Hot spots can have a deleterious impact on the total solar modules if individual solar cells are heated. So, the results show clearly that the solar cells are capable of self-generating considerable amounts of heat that should be dissipated very quickly to increase PV module's lifetime.
Abstract: This study presents a new model of the human image quality assessment process: the aim is to highlightthe foundations of the image quality metrics proposed in literature, by identifyingthe cognitive/physiological or mathematical principles of their development and the relation with the actual human quality assessment process. The model allows to createa novel categorization of objective and subjective image quality metrics. Our work includes an overview of the most used or effectiveobjective metrics in literature, and, for each of them, we underline its main characteristics, with reference to the rationale of the proposed model and categorization. From the results of this operation, we underline a problem that affects all the presented metrics: the fact that many aspects of human biasesare not taken in account at all. We then propose a possible methodology to address this issue.
Abstract: This study investigates the reliability of management earnings forecasts with reference to these two ingredients: verifiability and neutrality. Specifically, we examine the biasedness (or accuracy) of management earnings forecasts and company specific characteristics that can be associated with accuracy. Based on sample of 102 IPO prospectuses published for admission on NYSE Euronext Paris from 2002 to 2010, we found that these forecasts are on average optimistic and two of the five test variables, earnings variability and financial leverage are significant in explaining ex post bias. Acknowledging the possibility that the bias is the result of the managers’ forecasting behavior, we then examine whether managers decide to under-predict, over-predict or forecast accurately for self-serving purposes. Explicitly, we examine the role of financial distress, operating performance, ownership by insiders and the economy state in influencing managers’ forecasting preferences. We find that managers of distressed firms seem to over-predict future earnings. We also find that when managers are given more stock options, they tend to under-predict future earnings. Finally, we conclude that the management earnings forecasts are affected by an intentional bias due to managers’ forecasting preferences.
Abstract: This paper discusses the undesirable charge transfer by the parasitic capacitances of the input transistors in a voltage sense amplifier. Due to its intrinsic rail-to-rail voltage transition, the input sides are inevitably disturbed. It can possible disturb the stabilities of the reference voltage levels. Moreover, it becomes serious in multi-channel systems by altering them for other channels, and so degrades the linearity of the systems. In order to alleviate the internal node voltage transition, the internal node stabilization technique is proposed by utilizing an additional biasing circuit. It achieves 47% and 43% improvements for node stabilization and input referred disturbance, respectively.
Abstract: This work presents a proposal to perform contextual sentiment analysis using a supervised learning algorithm and disregarding the extensive training of annotators. To achieve this goal, a web platform was developed to perform the entire procedure outlined in this paper. The main contribution of the pipeline described in this article is to simplify and automate the annotation process through a system of analysis of congruence between the notes. This ensured satisfactory results even without using specialized annotators in the context of the research, avoiding the generation of biased training data for the classifiers. For this, a case
study was conducted in a blog of entrepreneurship. The experimental results were consistent with the literature related annotation using formalized process with experts.
Abstract: In this paper, motivated by the ideas of dependent weighted aggregation operators, we develop some new hesitant fuzzy dependent weighted aggregation operators to aggregate the input arguments taking the form of hesitant fuzzy numbers rather than exact numbers, or intervals. In fact, we propose three hesitant fuzzy dependent weighted averaging(HFDWA) operators, and three hesitant fuzzy dependent weighted geometric(HFDWG) operators based on different weight vectors, and the most prominent characteristic of these operators is that the associated weights only depend on the aggregated hesitant fuzzy numbers and can relieve the influence of unfair hesitant fuzzy numbers on the aggregated results by assigning low weights to those “false” and “biased” ones. Some examples are given to illustrated the efficiency of the proposed operators.
Abstract: This paper presents an idea for analog current comparison which compares input signal and reference currents with high speed and accuracy. Proposed circuit utilizes amplification properties of common gate configuration, where voltage variations of input current are amplified and a compared output voltage is developed. Cascaded inverter stages are used to generate final CMOS compatible output voltage. Power consumption of circuit can be controlled by the applied gate bias voltage. The comparator is designed and studied at 180nm CMOS process technology for a supply voltage of 3V.
Abstract: This paper aims to examine whether a bubble is present in the housing market of China. Thus, we use the housing price-to-income ratios and housing price-to-rent ratios of 35 cities from 1998 to 2010. The methods of the panel KSS unit root test with a Fourier function and the SPSM process are likewise used. The panel KSS unit root test with a Fourier function considers the problem of non-linearity and structural changes, and the SPSM process can avoid the stationary time series from dominating the result-generated bias. Through a rigorous empirical study, we determine that the housing price-to-income ratios are stationary in 34 of the 35 cities in China. Only Xining is non-stationary. The housing price-to-rent ratios are stationary in 32 of the 35 cities in China. Chengdu, Fuzhou, and Zhengzhou are non-stationary. Overall, the housing bubbles are not a serious problem in China at the time.
Abstract: Carrier scatterings in the inversion channel of MOSFET dominates the carrier mobility and hence drain current. This paper presents an analytical model of the subthreshold drain current incorporating the effective electron mobility model of the pocket implanted nano scale n-MOSFET. The model is developed by assuming two linear pocket profiles at the source and drain edges at the surface and by using the conventional drift-diffusion equation. Effective electron mobility model includes three scattering mechanisms, such as, Coulomb, phonon and surface roughness scatterings as well as ballistic phenomena in the pocket implanted n-MOSFET. The model is simulated for various pocket profile and device parameters as well as for various bias conditions. Simulation results show that the subthreshold drain current data matches the experimental data already published in the literature.
Abstract: Bone Anchored Hearing Implants (BAHI) are
routinely used in patients with conductive or mixed hearing loss, e.g.
if conventional air conduction hearing aids cannot be used. New
sound processors and new fitting software now allow the adjustment
of parameters such as loudness compression ratios or maximum
power output separately. Today it is unclear, how the choice of these
parameters influences aided speech understanding in BAHI users.
In this prospective experimental study, the effect of varying the
compression ratio and lowering the maximum power output in a
BAHI were investigated.
Twelve experienced adult subjects with a mixed hearing loss
participated in this study. Four different compression ratios (1.0; 1.3;
1.6; 2.0) were tested along with two different maximum power output
settings, resulting in a total of eight different programs. Each
participant tested each program during two weeks. A blinded Latin
square design was used to minimize bias.
For each of the eight programs, speech understanding in quiet and
in noise was assessed. For speech in quiet, the Freiburg number test
and the Freiburg monosyllabic word test at 50, 65, and 80 dB SPL
were used. For speech in noise, the Oldenburg sentence test was
administered.
Speech understanding in quiet and in noise was improved
significantly in the aided condition in any program, when compared
to the unaided condition. However, no significant differences were
found between any of the eight programs. In contrast, on a subjective
level there was a significant preference for medium compression
ratios of 1.3 to 1.6 and higher maximum power output.
Abstract: The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization problems. In the proposed GA, values in the genotypes are not real numbers but fuzzy numbers. Evolutionary processes in GA are extended so that GA can handle genotype instances with fuzzy numbers. The proposed method is applied to evolving neural networks with fuzzy weights and biases. Experimental results showed that fuzzy neural networks evolved by the fuzzy GA could model hidden target fuzzy functions well despite the fact that no training data was explicitly provided.