Abstract: In this paper, we study the oscillation of a class of second-order nonlinear neutral damped variable delay dynamic equations on time scales. By using a generalized Riccati transformation technique, we obtain some sufficient conditions for the oscillation of the equations. The results of this paper improve and extend some known results. We also illustrate our main results with some examples.
Abstract: High Performance Work Systems (HPWS) generally give rise to positive impacts on employees by increasing their commitments in workplaces. While some argued this actually have considerable negative impacts on employees with increasing possibilities of imposing strains caused by stress and intensity of such work places. Do stressful workplaces hamper employee commitment? The author has tried to find the answer by exploring linkages between HPWS practices and its impact on employees in Japanese organizations. How negative outcomes like job intensity and workplaces and job stressors can influence different forms of employees- commitments which can be a hindrance to their performance. Design: A close ended questionnaire survey was conducted amongst 16 large, medium and small sized Japanese companies from diverse industries around Chiba, Saitama, and Ibaraki Prefectures and in Tokyo from the month of October 2008 to February 2009. Questionnaires were aimed to the non managerial employees- perceptions of HPWS practices, their behavior, working life experiences in their work places. A total of 227 samples are used for analysis in the study. Methods: Correlations, MANCOVA, SEM Path analysis using AMOS software are used for data analysis in this study. Findings: Average non-managerial perception of HPWS adoption is significantly but negatively correlated to both work place Stressors and Continuous commitment, but positively correlated to job Intensity, Affective, Occupational and Normative commitments in different workplaces at Japan. The path analysis by SEM shows significant indirect relationship between Stressors and employee Affective organizational commitment and Normative organizational commitments. Intensity also has a significant indirect effect on Occupational commitments. HPWS has an additive effect on all the outcomes variables. Limitations: The sample size in this study cannot be a representative to the entire population of non-managerial employees in Japan. There were no respondents from automobile, pharmaceuticals, finance industries. The duration of the survey coincided in a period when Japan as most of the other countries is under going recession. Biases could not be ruled out completely. We must take cautions in interpreting the results of studies as they cannot be generalized. And the path analysis cannot provide the complete causality of the inter linkages between the variables used in the study. Originality: There have been limited studies on linkages in HPWS adoptions and their impacts on employees- behaviors and commitments in Japanese workplaces. This study may provide some ingredients for further research in the fields of HRM policies and practices and their linkages on different forms of employees- commitments.
Abstract: This study examines the relationships between foreign
aid, levels of schooling and democracy for Pakistan using the ARDL
cointegration approach. The results of study provide strong evidence
for fairly robust long run as well as short run relationships among
these variables for the period 1973-2008. The results state that
foreign aid and primary school enrollments have negative impact on
democracy index and high school enrollments have positive impact
on democracy index in Pakistan. The study suggests for promotion of
education levels and relies on local resources instead of foreign aid
for a good quality of political institutions in Pakistan.
Abstract: In this paper, the concepts of dichotomous logistic
regression (DLR) with leave-one-out (L-O-O) were discussed. To
illustrate this, the L-O-O was run to determine the importance of the
simulation conditions for robust test of spread procedures with good
Type I error rates. The resultant model was then evaluated. The
discussions included 1) assessment of the accuracy of the model, and
2) parameter estimates. These were presented and illustrated by
modeling the relationship between the dichotomous dependent
variable (Type I error rates) with a set of independent variables (the
simulation conditions). The base SAS software containing PROC
LOGISTIC and DATA step functions can be making used to do the
DLR analysis.
Abstract: This paper introduces a technique of distortion
estimation in image watermarking using Genetic Programming (GP).
The distortion is estimated by considering the problem of obtaining a
distorted watermarked signal from the original watermarked signal as
a function regression problem. This function regression problem is
solved using GP, where the original watermarked signal is
considered as an independent variable. GP-based distortion
estimation scheme is checked for Gaussian attack and Jpeg
compression attack. We have used Gaussian attacks of different
strengths by changing the standard deviation. JPEG compression
attack is also varied by adding various distortions. Experimental
results demonstrate that the proposed technique is able to detect the
watermark even in the case of strong distortions and is more robust
against attacks.
Abstract: The main goal in this paper is to quantify the quality of
different techniques for radiation treatment plans, a back-propagation
artificial neural network (ANN) combined with biomedicine theory
was used to model thirteen dosimetric parameters and to calculate
two dosimetric indices. The correlations between dosimetric indices
and quality of life were extracted as the features and used in the ANN
model to make decisions in the clinic. The simulation results show
that a trained multilayer back-propagation neural network model can
help a doctor accept or reject a plan efficiently. In addition, the
models are flexible and whenever a new treatment technique enters
the market, the feature variables simply need to be imported and the
model re-trained for it to be ready for use.
Abstract: This study examines the relevance of disclosure
practices in improving the accountability and transparency of
religious nonprofit organizations (RNPOs). The assessment of
disclosure is based on the annual returns of RNPOs for the financial
year 2010. In order to quantify the information disclosed in the
annual returns, partial disclosure indexes of basic information (BI)
disclosure index, financial information (FI) disclosure index and
governance information (GI) disclosure index have been built which
takes into account the content of information items in the annual
returns. The empirical evidence obtained revealed low disclosure
practices among RNPOs in the sample. The multiple regression
results showed that the organizational attribute of the board size
appeared to be the most significant predictor for both partial index on
the extent of BI disclosure index, and FI disclosure index. On the
other hand, the extent of financial information disclosure is related to
the amount of donation received by RNPOs. On GI disclosure index,
the existence of an external audit appeared to be significant variable.
This study has contributed to the academic literature in providing
empirical evidence of the disclosure practices among RNPOs.
Abstract: Soil organic carbon (SOC) plays a key role in soil
fertility, hydrology, contaminants control and acts as a sink or source
of terrestrial carbon content that can affect the concentration of
atmospheric CO2. SOC supports the sustainability and quality of
ecosystems, especially in semi-arid region. This study was
conducted to determine relative importance of 13 different
exploratory climatic, soil and geometric factors on the SOC contents
in one of the semiarid watershed zones in Iran. Two methods
canonical discriminate analysis (CDA) and feed-forward back
propagation neural networks were used to predict SOC. Stepwise
regression and sensitivity analysis were performed to identify
relative importance of exploratory variables. Results from sensitivity
analysis showed that 7-2-1 neural networks and 5 inputs in CDA
models output have highest predictive ability that explains %70 and
%65 of SOC variability. Since neural network models outperformed
CDA model, it should be preferred for estimating SOC.
Abstract: This study investigated students- perception of self
efficacy and anxiety in acquiring English language, and consequently
examined the relationship existing among the independent variables,
confounding variables and students- performances in the English
language. The researcher tested the research hypotheses using a
sample group of 318 respondents out of the population size of 400
students. The results obtained revealed that there was a significant
moderate negative relationship between English language anxiety
and performance in English language, but no significant relationship
between self-efficacy and English language performance, among the
middle-school students. There was a significant moderate negative
relationship between English language anxiety and self-efficacy. It
was discovered that general self-efficacy and English language
anxiety represented a significantly more powerful set of predictors
than the set of confounding variables. Thus, the study concluded that
English language anxiety and general self-efficacy were significant
predictors of English language performance among middle-school
students in Satri Si Suriyothai School.
Abstract: In many data mining applications, it is a priori known
that the target function should satisfy certain constraints imposed
by, for example, economic theory or a human-decision maker. In this
paper we consider partially monotone prediction problems, where the
target variable depends monotonically on some of the input variables
but not on all. We propose a novel method to construct prediction
models, where monotone dependences with respect to some of
the input variables are preserved by virtue of construction. Our
method belongs to the class of mixture models. The basic idea is to
convolute monotone neural networks with weight (kernel) functions
to make predictions. By using simulation and real case studies,
we demonstrate the application of our method. To obtain sound
assessment for the performance of our approach, we use standard
neural networks with weight decay and partially monotone linear
models as benchmark methods for comparison. The results show that
our approach outperforms partially monotone linear models in terms
of accuracy. Furthermore, the incorporation of partial monotonicity
constraints not only leads to models that are in accordance with the
decision maker's expertise, but also reduces considerably the model
variance in comparison to standard neural networks with weight
decay.
Abstract: Variable ordering heuristics are used in constraint satisfaction algorithms. Different characteristics of various variable ordering heuristics are complementary. Therefore we have tried to get the advantages of all heuristics to improve search algorithms performance for solving constraint satisfaction problems. This paper considers combinations based on products and quotients, and then a newer form of combination based on weighted sums of ratings from a set of base heuristics, some of which result in definite improvements in performance.
Abstract: Eco-driving allows the driver to optimize his/her behaviour in order to achieve several types of benefits: reducing pollution emissions, increasing road safety, and fuel saving. One of the main rules for adopting eco-driving is to anticipate the traffic events by avoiding strong acceleration or braking and maintaining a steady speed when possible. Therefore, drivers have to comply with speed limits and time headway. The present study explored the role of three types of motivation and social norms in predicting French drivers- intentions to comply with speed limits and time headway as eco-driving practices as well as examine the variations according to gender and age. 1234 drivers with ages between 18 and 75 years old filled in a questionnaire which was presented as part of an online survey aiming to better understand the drivers- road habits. It included items assessing: a) behavioural intentions to comply with speed limits and time headway according to three types of motivation: reducing pollution emissions, increasing road safety, and fuel saving, b) subjective and descriptive social norms regarding the intention to comply with speed limits and time headway, and c) sociodemographical variables. Drivers expressed their intention to frequently comply with speed limits and time headway in the following 6 months; however, they showed more intention to comply with speed limits as compared to time headway regardless of the type of motivation. The subjective injunctive norms were significantly more important in predicting drivers- intentions to comply with speed limits and time headway as compared to the descriptive norms. In addition, the most frequently reported type of motivation for complying with speed limits and time headway was increasing road safety followed by fuel saving and reducing pollution emissions, hence underlining a low motivation to practice eco-driving. Practical implications of the results are discussed.
Abstract: Modern manufacturing facilities are large scale,
highly complex, and operate with large number of variables under
closed loop control. Early and accurate fault detection and diagnosis
for these plants can minimise down time, increase the safety of plant
operations, and reduce manufacturing costs. Fault detection and
isolation is more complex particularly in the case of the faulty analog
control systems. Analog control systems are not equipped with
monitoring function where the process parameters are continually
visualised. In this situation, It is very difficult to find the relationship
between the fault importance and its consequences on the product
failure. We consider in this paper an approach to fault detection and
analysis of its effect on the production quality using an adaptive
centring and scaling in the pickling process in cold rolling. The fault
appeared on one of the power unit driving a rotary machine, this
machine can not track a reference speed given by another machine.
The length of metal loop is then in continuous oscillation, this affects
the product quality. Using a computerised data acquisition system,
the main machine parameters have been monitored. The fault has
been detected and isolated on basis of analysis of monitored data.
Normal and faulty situation have been obtained by an artificial neural
network (ANN) model which is implemented to simulate the normal
and faulty status of rotary machine. Correlation between the product
quality defined by an index and the residual is used to quality
classification.
Abstract: Having a very many number of pipelines all over the
country, Iran is one of the countries consists of various ecosystems
with variable degrees of fragility and robusticity as well as
geographical conditions. This study presents a state-of-the-art method
to estimate environmental risks of pipelines by recommending
rational equations including FES, URAS, SRS, RRS, DRS, LURS
and IRS as well as FRS to calculate the risks. This study was carried
out by a relative semi-quantitative approach based on land uses and
HVAs (High-Value Areas). GIS as a tool was used to create proper
maps regarding the environmental risks, land uses and distances. The
main logic for using the formulas was the distance-based approaches
and ESI as well as intersections. Summarizing the results of the
study, a risk geographical map based on the ESIs and final risk score
(FRS) was created. The study results showed that the most sensitive
and so of high risk area would be an area comprising of mangrove
forests located in the pipeline neighborhood. Also, salty lands were
the most robust land use units in the case of pipeline failure
circumstances. Besides, using a state-of-the-art method, it showed
that mapping the risks of pipelines out with the applied method is of
more reliability and convenience as well as relative
comprehensiveness in comparison to present non-holistic methods for
assessing the environmental risks of pipelines. The focus of the
present study is “assessment" than that of “management". It is
suggested that new policies are to be implemented to reduce the
negative effects of the pipeline that has not yet been constructed
completely
Abstract: This article presents a method for elections between the members of a group that is founded by fuzzy logic. Linguistic variables are objects for decision on election cards and deduction is based on t-norms and s-norms. In this election-s method election cards are questionnaire. The questionnaires are comprised of some questions with some choices. The choices are words from natural language. Presented method is accompanied by center of gravity (COG) defuzzification added up to a computer program by MATLAB. Finally the method is illustrated by solving two examples; choose a head for a research group-s members and a representative for students.
Abstract: Climate change leading to global warming affects the
earth through many different ways such as weather (temperature, precipitation, humidity and the other parameters of weather), snow coverage and ice melting, sea level rise, hydrological cycles, quality of water, agriculture, forests, ecosystems and health. One of the most
affected areas by climate change is hydrology and water resources.
Regions where majority of runoff consists of snow melt are more
sensitive to climate change. The first step of climate change studies
is to establish trends of significant climate variables including precipitation,
temperature and flow data to detect any potential climate
change impacts already happened. Two popular non-parametric trend
analysis methods, Mann-Kendal and Spearman-s Rho were applied
to Upper Euphrates Basin (Turkey) to detect trends of precipitation,
temperatures (maximum, minimum and average) and streamflow.
Abstract: A suspension bridge is the most suitable type of structure for a long-span bridge due to rational use of structural materials. Increased deformability, which is conditioned by appearance of the elastic and kinematic displacements, is the major disadvantage of suspension bridges. The problem of increased kinematic displacements under the action of non-symmetrical load can be solved by prestressing. The prestressed suspension bridge with the span of 200 m was considered as an object of investigations. The cable truss with the cross web was considered as the main load carrying structure of the prestressed suspension bridge. The considered cable truss was optimized by 47 variable factors using Genetic algorithm and FEM program ANSYS. It was stated, that the maximum total displacements are reduced up to 29.9% by using of the cable truss with the rational characteristics instead of the single cable in the case of the worst situated load.
Abstract: The tray/multi-tray distillation process is a topic that
has been investigated to great detail over the last decade by many
teams such as Jubran et al. [1], Adhikari et al. [2], Mowla et al. [3],
Shatat et al. [4] and Fath [5] to name a few. A significant amount of
work and effort was spent focusing on modeling and/simulation of
specific distillation hardware designs. In this work, we have focused
our efforts on investigating and gathering experimental data on
several engineering and design variables to quantify their influence
on the yield of the multi-tray distillation process. Our goals are to
generate experimental performance data to bridge some existing gaps
in the design, engineering, optimization and theoretical modeling
aspects of the multi-tray distillation process.
Abstract: Optimization of extraction of phenolic compounds
from Avicennia marina using response surface methodology was
carried out during the present study. Five levels, three factors
rotatable design (CCRD) was utilized to examine the optimum
combination of extraction variables based on the TPC of Avicennia
marina leaves. The best combination of response function was 78.41
°C, drying temperature; 26.18°C; extraction temperature and 36.53
minutes of extraction time. However, the procedure can be promptly
extended to the study of several others pharmaceutical processes like
purification of bioactive substances, drying of extracts and
development of the pharmaceutical dosage forms for the benefit of
consumers.
Abstract: This paper provides the design steps of a robust Linear
Matrix Inequality (LMI) based iterative multivariable PID controller
whose duty is to drive a sample power system that comprises a
synchronous generator connected to a large network via a step-up
transformer and a transmission line. The generator is equipped with
two control-loops, namely, the speed/power (governor) and voltage
(exciter). Both loops are lumped in one where the error in the
terminal voltage and output active power represent the controller
inputs and the generator-exciter voltage and governor-valve position
represent its outputs. Multivariable PID is considered here because of
its wide use in the industry, simple structure and easy
implementation. It is also preferred in plants of higher order that
cannot be reduced to lower ones. To improve its robustness to
variation in the controlled variables, H∞-norm of the system transfer
function is used. To show the effectiveness of the controller, divers
tests, namely, step/tracking in the controlled variables, and variation
in plant parameters, are applied. A comparative study between the
proposed controller and a robust H∞ LMI-based output feedback is
given by its robustness to disturbance rejection. From the simulation
results, the iterative multivariable PID shows superiority.