International Science Index
Predictive Semi-Empirical NOx Model for Diesel Engine
Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O2 concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model. Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO2/NOx ratio etc.
Statistical and Land Planning Study of Tourist Arrivals in Greece during 2005-2016
During the last 10 years, in spite of the economic crisis, the number of tourists arriving in Greece has increased, particularly during the tourist season from April to October. In this paper, the number of annual tourist arrivals is studied to explore their preferences with regard to the month of travel, the selected destinations, as well the amount of money spent. The collected data are processed with statistical methods, yielding numerical and graphical results. From the computation of statistical parameters and the forecasting with exponential smoothing, useful conclusions are arrived at that can be used by the Greek tourism authorities, as well as by tourist organizations, for planning purposes for the coming years. The results of this paper and the computed forecast can also be used for decision making by private tourist enterprises that are investing in Greece. With regard to the statistical methods, the method of Simple Exponential Smoothing of time series of data is employed. The search for a best forecast for 2017 and 2018 provides the value of the smoothing coefficient. For all statistical computations and graphics Microsoft Excel is used.
Main Tendencies of Youth Unemployment and the Regulation Mechanisms for Decreasing Its Rate in Georgia
The modern world faces huge challenges. Globalization changed the socio-economic conditions of many countries. The current processes in the global environment have a different impact on countries with different cultures. However, an alleviation of poverty and improvement of living conditions is still the basic challenge for the majority of countries, because much of the population still lives under the official threshold of poverty. It is very important to stimulate youth employment. In order to prepare young people for the labour market, it is essential to provide them with the appropriate professional skills and knowledge. It is necessary to plan efficient activities for decreasing an unemployment rate and for developing the perfect mechanisms for regulation of a labour market. Such planning requires thorough study and analysis of existing reality, as well as development of corresponding mechanisms. Statistical analysis of unemployment is one of the main platforms for regulation of the labour market key mechanisms. The corresponding statistical methods should be used in the study process. Such methods are observation, gathering, grouping, and calculation of the generalized indicators. Unemployment is one of the most severe socioeconomic problems in Georgia. According to the past as well as the current statistics, unemployment rates always have been the most problematic issue to resolve for policy makers. Analytical works towards to the above-mentioned problem will be the basis for the next sustainable steps to solve the main problem. The results of the study showed that the choice of young people is not often due to their inclinations, their interests and the labour market demand. That is why the wrong professional orientation of young people in most cases leads to their unemployment. At the same time, it was shown that there are a number of professions in the labour market with a high demand because of the deficit the appropriate specialties. To achieve healthy competitiveness in youth employment, it is necessary to formulate regional employment programs with taking into account the regional infrastructure specifications.
Analysis of Fertilizer Effect in the Tilapia Growth of Mozambique (Oreochromis mossambicus)
This paper analyses the effect of fertilizer (organic and
inorganic) in the growth of tilapia. An experiment was implemented
in the Aquapesca Company of Mozambique; there were considered
four different treatments. Each type of fertilizer was applied in two of
these treatments; a feed was supplied to the third treatment, and the
fourth was taken as control. The weight and length of the tilapia were
used as the growth parameters, and to measure the water quality, the
physical-chemical parameters were registered. The results show that
the weight and length were different for tilapias cultivated in different
treatments. These differences were evidenced mainly by organic and
feed treatments, where there was the largest and smallest value of
these parameters, respectively. In order to prove that these differences
were caused only by applied treatment without interference for the
aquatic environment, a Fisher discriminant analysis was applied,
which confirmed that the treatments were exposed to the same
The Changing Trend of Collaboration Patterns in the Social Sciences: Institutional Influences on Academic Research in Korea, 2013-2016
Collaborative research has become more prevalent and important across disciplines because it stimulates innovation and interaction between scholars. Seeing as existing studies relatively disregarded the institutional conditions triggering collaborative research, this work aims to analyze the changing trend in collaborative work patterns among Korean social scientists. The focus of this research is the performance of social scientists who received research grants through the government’s Social Science Korea (SSK) program. Using quantitative statistical methods, collaborative research patterns in a total of 2,354 papers published under the umbrella of the SSK program in peer-reviewed scholarly journals from 2013 to 2016 were examined to identify changing trends and triggering factors in collaborative research. A notable finding is that the share of collaborative research is overwhelmingly higher than that of individual research. In particular, levels of collaborative research surpassed 70%, increasing much quicker compared to other research done in the social sciences. Additionally, the most common composition of collaborative research was for two or three researchers to conduct joint research as coauthors, and this proportion has also increased steadily. Finally, a strong association between international journals and co-authorship patterns was found for the papers published by SSK program researchers from 2013 to 2016. The SSK program can be seen as the driving force behind collaboration between social scientists. Its emphasis on competition through a merit-based financial support system along with a rigorous evaluation process seems to have influenced researchers to cooperate with those who have similar research interests.
UK GAAP and IFRS Standards: Similarities and Differences
This paper aimed to help researchers and international companies to the differences and similarities between IFRS (International financial reporting standards) and UK GAAP or UK accounting principles, and to the accounting changes between standard setting of the International Accounting Standards Board and the Accounting Standards Board in United Kingdom. We will use in this study statistical methods to calculate similarities and difference frequencies between the UK standards and IFRS standards, according to the PricewaterhouseCoopers report in 2005. We will use the one simple test to confirm or refuse our hypothesis. In conclusion, we found that the gap between UK GAAP and IFRS is small.
Summarizing Data Sets for Data Mining by Using Statistical Methods in Coastal Engineering
Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.
Inner and Outer School Contextual Factors Associated with Poor Performance of Grade 12 Students: A Case Study of an Underperforming High School in Mpumalanga, South Africa
Often a Grade 12 certificate is perceived as a passport to tertiary education and the minimum requirement to enter the world of work. In spite of its importance, many students do not make this milestone in South Africa. It is important to find out why so many students still fail in spite of transformation in the education system in the post-apartheid era. Given the complexity of education and its context, this study adopted a case study design to examine one historically underperforming high school in Bushbuckridge, Mpumalanga Province, South Africa in 2013. The aim was to gain a understanding of the inner and outer school contextual factors associated with the high failure rate among Grade 12 students. Government documents and reports were consulted to identify factors in the district and the village surrounding the school and a student survey was conducted to identify school, home and student factors. The randomly-sampled half of the population of Grade 12 students (53) participated in the survey and quantitative data are analyzed using descriptive statistical methods. The findings showed that a host of factors is at play. The school is located in a village within a municipality which has been one of the poorest three municipalities in South Africa and the lowest Grade 12 pass rate in the Mpumalanga province. Moreover, over half of the families of the students are single parents, 43% are unemployed and the majority has a low level of education. In addition, most families (83%) do not have basic study materials such as a dictionary, books, tables, and chairs. A significant number of students (70%) are over-aged (+19 years old); close to half of them (49%) are grade repeaters. The school itself lacks essential resources, namely computers, science laboratories, library, and enough furniture and textbooks. Moreover, teaching and learning are negatively affected by the teachers’ occasional absenteeism, inadequate lesson preparation, and poor communication skills. Overall, the continuous low performance of students in this school mirrors the vicious circle of multiple negative conditions present within and outside of the school. The complexity of factors associated with the underperformance of Grade 12 students in this school calls for a multi-dimensional intervention from government and stakeholders. One important intervention should be the placement of over-aged students and grade-repeaters in suitable educational institutions for the benefit of other students.
A Sociological Study of Rural Women Attitudes toward Education, Health and Work outside Home in Beheira Governorate, Egypt
This research was performed to evaluate the attitudes of rural women towards education, health and work outside the home. The study was based on a random sample of 147 rural women, Kafr-Rahmaniyah village was chosen for the study because its life expectancy at birth for females, education and percentage of females in the labor force, were the highest in the district. The study data were collected from rural female respondents, using a face-to-face questionnaire. In addition, the study estimated several factors like age, main occupation, family size, monthly household income, geographic cosmopolites, and degree of social participation for rural women respondents. Using Statistical Package for the Social Sciences (SPSS), data were analyzed by non-parametric statistical methods. The main finding in this study was a significant relationship between each of the previous variables and each of rural women’s attitudes toward education, health, and work outside home. The study concluded with some recommendations. The most important element is ensuring attention to rural women’s needs, requirements and rights via raising their health awareness, education and their contributions in their society.
Mathematical Study for Traffic Flow and Traffic Density in Kigali Roads
This work investigates a mathematical study for traffic flow and traffic density in Kigali city roads and the data collected from the national police of Rwanda in 2012. While working on this topic, some mathematical models were used in order to analyze and compare traffic variables. This work has been carried out on Kigali roads specifically at roundabouts from Kigali Business Center (KBC) to Prince House as our study sites. In this project, we used some mathematical tools to analyze the data collected and to understand the relationship between traffic variables. We applied the Poisson distribution method to analyze and to know the number of accidents occurred in this section of the road which is from KBC to Prince House. The results show that the accidents that occurred in 2012 were at very high rates due to the fact that this section has a very narrow single lane on each side which leads to high congestion of vehicles, and consequently, accidents occur very frequently. Using the data of speeds and densities collected from this section of road, we found that the increment of the density results in a decrement of the speed of the vehicle. At the point where the density is equal to the jam density the speed becomes zero. The approach is promising in capturing sudden changes on flow patterns and is open to be utilized in a series of intelligent management strategies and especially in noncurrent congestion effect detection and control.
Self-Perceived Employability of Students of International Relations of University of Warmia and Mazury in Poland
Nowadays, graduates should be prepared for serious challenges in the internal and external labor market. The notion that a degree is a “passport to employment” has been relegated to the past. In the last few years a phenomenon in the form of the increasing unemployment of highly educated young people in EU countries, including Poland has been observed. Empirical studies were conducted among Polish students in the scope of the so-called self-perceived employability review. In this study, a special scale was used which consisted of 19 statements regarding five components: student’s perception of university; field of study; self-belief; state of the external labor market; and, personal knowledge management. The respondent group consisted of final-year master’s students of International Relations at the University of Warmia and Mazury in Olsztyn, Poland. The findings of the empirical studies were compiled using statistical methods: descriptive statistics and inferential statistics. In general, in light of the conducted studies, the self-perceived employability of the Polish students was not high. Limitations of the studies were discussed, as well as the implications for future research in the scope of the students’ employability.
Statistical Analysis of Interferon-γ for the Effectiveness of an Anti-Tuberculous Treatment
Tuberculosis (TB) is a potentially serious infectious disease that remains a health concern. The Interferon Gamma Release Assay (IGRA) is a blood test to find out if an individual is tuberculous positive or negative. This study applies statistical analysis to the clinical data of interferon-gamma levels of seventy-three subjects who diagnosed pulmonary TB in an anti-tuberculous treatment. Data analysis is performed to determine if there is a significant decline in interferon-gamma levels for the subjects during a period of six months, and to infer if the anti-tuberculous treatment is effective.
Using Gaussian Process in Wind Power Forecasting
The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given.
A Brief Study about Nonparametric Adherence Tests
The statistical study has become indispensable for various fields of knowledge. Not any different, in Geotechnics the study of probabilistic and statistical methods has gained power considering its use in characterizing the uncertainties inherent in soil properties. One of the situations where engineers are constantly faced is the definition of a probability distribution that represents significantly the sampled data. To be able to discard bad distributions, goodness-of-fit tests are necessary. In this paper, three non-parametric goodness-of-fit tests are applied to a data set computationally generated to test the goodness-of-fit of them to a series of known distributions. It is shown that the use of normal distribution does not always provide satisfactory results regarding physical and behavioral representation of the modeled parameters.
The Comparison of Parental Childrearing Styles and Anxiety in Children with Stuttering and Normal Population
Family has a crucial role in maintaining the
physical, social and mental health of the children. Most of the
mental and anxiety problems of children reflect the complex
interpersonal situations among family members, especially parents.
In other words, anxiety problems of the children are correlated
with deficit relationships of family members and improper
childrearing styles. The parental child rearing styles leads to
positive and negative consequences which affect the children’s
mental health. Therefore, the present research was aimed to
compare the parental childrearing styles and anxiety of children
with stuttering and normal population. It was also aimed to study
the relationship between parental child rearing styles and anxiety
of children. The research sample included 54 boys with stuttering
and 54 normal boys who were selected from the children (boys) of
Tehran, Iran in the age range of 5 to 8 years in 2013. In order to
collect data, Baum-rind Childrearing Styles Inventory and Spence
Parental Anxiety Inventory were used. Appropriate descriptive
statistical methods and multivariate variance analysis and t test for
independent groups were used to test the study hypotheses.
Statistical data analyses demonstrated that there was a significant
difference between stuttering boys and normal boys in anxiety (t =
7.601, p< 0.01); but there was no significant difference between
stuttering boys and normal boys in parental childrearing styles (F =
0.129). There was also not found significant relationship between
parental childrearing styles and children anxiety (F = 0.135, p<
0.05). It can be concluded that the influential factors of children’s
society are parents, school, teachers, peers and media. So, parental
childrearing styles are not the only influential factors on anxiety of
children, and other factors including genetic, environment and
child experiences are effective in anxiety as well. Details are
A Study on the Relation among Primary Care Professionals Serving the Disadvantaged Community, Socioeconomic Status, and Adverse Health Outcome
During the post-Civil War era, the city of Nashville,
Tennessee, had the highest mortality rate in the United States. The
elevated death and disease rates among former slaves were
attributable to lack of quality healthcare. To address the paucity of
healthcare services, Meharry Medical College, an institution with the
mission of educating minority professionals and serving the
underserved population, was established in 1876.
Purpose: The social ecological framework and partial least squares
(PLS) path modeling were used to quantify the impact of
socioeconomic status and adverse health outcome on primary care
professionals serving the disadvantaged community. Thus, the study
results could demonstrate the accomplishment of the College’s
mission of training primary care professionals to serve in underserved
Methods: Various statistical methods were used to analyze alumni
data from 1975 – 2013. K-means cluster analysis was utilized to
identify individual medical and dental graduates in the cluster groups
of the practice communities (Disadvantaged or Non-disadvantaged
Communities). Discriminant analysis was implemented to verify the
classification accuracy of cluster analysis. The independent t-test was
performed to detect the significant mean differences of respective
clustering and criterion variables. Chi-square test was used to test if
the proportions of primary care and non-primary care specialists are
consistent with those of medical and dental graduates practicing in
the designated community clusters. Finally, the PLS path model was
constructed to explore the construct validity of analytic model by
providing the magnitude effects of socioeconomic status and adverse
health outcome on primary care professionals serving the
Results: Approximately 83% (3,192/3,864) of Meharry Medical
College’s medical and dental graduates from 1975 to 2013 were
practicing in disadvantaged communities. Independent t-test confirmed the content validity of the cluster analysis model. Also, the
PLS path modeling demonstrated that alumni served as primary care
professionals in communities with significantly lower socioeconomic
status and higher adverse health outcome (p < .001). The PLS path
modeling exhibited the meaningful interrelation between primary
care professionals practicing communities and surrounding
environments (socioeconomic statues and adverse health outcome),
which yielded model reliability, validity, and applicability.
Conclusion: This study applied social ecological theory and
analytic modeling approaches to assess the attainment of Meharry
Medical College’s mission of training primary care professionals to
serve in underserved areas, particularly in communities with low
socioeconomic status and high rates of adverse health outcomes. In
summary, the majority of medical and dental graduates from Meharry
Medical College provided primary care services to disadvantaged
communities with low socioeconomic status and high adverse health
outcome, which demonstrated that Meharry Medical College has
fulfilled its mission. The high reliability, validity, and applicability of
this model imply that it could be replicated for comparable
universities and colleges elsewhere.
The Strengths and Limitations of the Statistical Modeling of Complex Social Phenomenon: Focusing on SEM, Path Analysis, or Multiple Regression Models
This paper analyzes the conceptual framework of three
statistical methods, multiple regression, path analysis, and structural
equation models. When establishing research model of the statistical
modeling of complex social phenomenon, it is important to know the
strengths and limitations of three statistical models. This study
explored the character, strength, and limitation of each modeling and
suggested some strategies for accurate explaining or predicting the
causal relationships among variables. Especially, on the studying of
depression or mental health, the common mistakes of research
modeling were discussed.
Earthquake Classification in Molluca Collision Zone Using Conventional Statistical Methods
Molluca Collision Zone is located at the junction of
the Eurasian, Australian, Pacific and the Philippines plates. Between
the Sangihe arc, west of the collision zone, and to the east of
Halmahera arc is active collision and convex toward the Molluca Sea.
This research will analyze the behavior of earthquake occurrence in
Molluca Collision Zone related to the distributions of an earthquake
in each partition regions, determining the type of distribution of a
occurrence earthquake of partition regions, and the mean occurence
of earthquakes each partition regions, and the correlation between the
partitions region. We calculate number of earthquakes using partition
method and its behavioral using conventional statistical methods. In
this research, we used data of shallow earthquakes type and its
magnitudes ≥4 SR (period 1964-2013). From the results, we can
classify partitioned regions based on the correlation into two classes:
strong and very strong. This classification can be used for early
warning system in disaster management.
Variation in the Traditional Knowledge of Curcuma longa L. in North-Eastern Algeria
Curcuma longa L. (Zingiberaceae), commonly known
as turmeric, has a long history of traditional uses for culinary
purposes as a spice and a food colorant. The present study aimed to
document the ethnobotanical knowledge about Curcuma longa, and
to assess the variation in the herbalists’ experience in Northeastern
Algeria. Data were collected using semi-structured questionnaires
and direct interviews with 30 herbalists. Ethnobotanical indices,
including the fidelity level (FL%), the relative frequency citation
(RFC), and use value (UV) were determined by quantitative methods.
Diversity in the level of knowledge was analyzed using univariate,
non-parametric, and multivariate statistical methods. Three main
categories of uses were recorded for C. longa: for food, for medicine,
and for cosmetic purposes. As a medicine, turmeric was used for the
treatment of gastrointestinal, dermatological, and hepatic diseases.
Medicinal and food uses were correlated with both forms of
preparation (rhizome and powder). The age group did not influence
the use. Multivariate analyses showed a significant variation in
traditional knowledge, associated with the use value, origin, quality,
and efficacy of the drug. The findings suggested that the geographical
origin of C. longa affected the use in Algeria.
Forecasting Rainfall in Thailand: A Case Study of Nakhon Ratchasima Province
In this paper, we study the rainfall using a time series
for weather stations in Nakhon Ratchasima province in Thailand by
various statistical methods to enable us to analyse the behaviour of
rainfall in the study areas. Time-series analysis is an important tool in
modelling and forecasting rainfall. The ARIMA and Holt-Winter
models were built on the basis of exponential smoothing. All the
models proved to be adequate. Therefore it is possible to give
information that can help decision makers establish strategies for the
proper planning of agriculture, drainage systems and other water
resource applications in Nakhon Ratchasima province. We obtained
the best performance from forecasting with the ARIMA
Data Mining in Medicine Domain Using Decision Trees and Vector Support Machine
In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
A Follow–Up Study of Bachelor of Science Graduates in Applied Statistics from Suan Sunandha Rajabhat University during the 1999-2012 Academic Years
The purpose of this study is to follow – up the graduated students of Bachelor of Science in Applied Statistics from Suan Sunandha Rajabhat University (SSRU) during the 1999 – 2012 academic years and to provide the fundamental guideline for developing the current curriculum according to Thai Qualifications Framework for Higher Education (TQF: HEd). The sample was collected from 75 graduates by interview and online questionnaire. The content covered 5 subjects were Ethics and Moral, Knowledge, Cognitive Skills, Interpersonal Skill and Responsibility, Numerical Analysis as well as Communication and Information Technology Skills. Data were analyzed by using statistical methods as percentiles, means, standard deviation, t- tests, and F- tests. The findings showed that samples were mostly female had less than 26 years old. The majority of graduates had income in the range of 10,001-20,000 Baht and experience range were 2-5 years. In addition, overall opinions from receiving knowledge to apply to work were at agree; mean score was 3.97 and standard deviation was 0.40. In terms of, the hypothesis testing’s result indicate gender only had different opinion at a significance level of 0.05.
Automated Process Quality Monitoring with Prediction of Fault Condition Using Measurement Data
Detection of incipient abnormal events is important to
improve safety and reliability of machine operations and reduce losses
caused by failures. Improper set-ups or aligning of parts often leads to
severe problems in many machines. The construction of prediction
models for predicting faulty conditions is quite essential in making
decisions on when to perform machine maintenance. This paper
presents a multivariate calibration monitoring approach based on the
statistical analysis of machine measurement data. The calibration
model is used to predict two faulty conditions from historical reference
data. This approach utilizes genetic algorithms (GA) based variable
selection, and we evaluate the predictive performance of several
prediction methods using real data. The results shows that the
calibration model based on supervised probabilistic principal
component analysis (SPPCA) yielded best performance in this work.
By adopting a proper variable selection scheme in calibration models,
the prediction performance can be improved by excluding
non-informative variables from their model building steps.
Texture Feature Extraction of Infrared River Ice Images using Second-Order Spatial Statistics
Ice cover County has a significant impact on rivers as it affects with the ice melting capacity which results in flooding, restrict navigation, modify the ecosystem and microclimate. River ices are made up of different ice types with varying ice thickness, so surveillance of river ice plays an important role. River ice types are captured using infrared imaging camera which captures the images even during the night times. In this paper the river ice infrared texture images are analysed using first-order statistical methods and secondorder statistical methods. The second order statistical methods considered are spatial gray level dependence method, gray level run length method and gray level difference method. The performance of the feature extraction methods are evaluated by using Probabilistic Neural Network classifier and it is found that the first-order statistical method and second-order statistical method yields low accuracy. So the features extracted from the first-order statistical method and second-order statistical method are combined and it is observed that the result of these combined features (First order statistical method + gray level run length method) provides higher accuracy when compared with the features from the first-order statistical method and second-order statistical method alone.
Cryogenic Freezing Process Optimization Based On Desirability Function on the Path of Steepest Ascent
This paper presents a comparative study of statistical methods for the multi-response surface optimization of a cryogenic freezing process. Taguchi design and analysis and steepest ascent methods based on the desirability function were conducted to ascertain the influential factors of a cryogenic freezing process and their optimal levels. The more preferable levels of the set point, exhaust fan speed, retention time and flow direction are set at -90oC, 20 Hz, 18 minutes and Counter Current, respectively. The overall desirability level is 0.7044.
Breast Motion and Discomfort of Chinese Women in Three Breast Support Conditions
Breast motion and discomfort has been studied in
Australia, Britain and the United States, while little information was
known about the breast motion conditions of Chinese women. The aim
of this paper was to study the breast motion and discomfort of Chinese
women in no bra condition, daily bra condition and sports bra
condition. Breast motion and discomfort of 8 participants was assessed
during walking at 5km h-1 and running at 10km h-1. Statistical methods
were used to analyze the difference and relationship between breast
displacement, perceived breast motion and breast discomfort. Three
indexes were developed to evaluate the functions of bras on reducing
objective breast motion, subjective breast motion and breast
discomfort. The result showed that breast motion of Chinese women
was smaller than previous research, which may be resulted from
smaller breast size in Asian women.
A Novel Approach to Handle Uncertainty in Health System Variables for Hospital Admissions
Hospital staff and managers are under pressure and
concerned for effective use and management of scarce resources. The
hospital admissions require many decisions that have complex and
uncertain consequences for hospital resource utilization and patient
flow. It is challenging to predict risk of admissions and length of stay
of a patient due to their vague nature. There is no method to capture
the vague definition of admission of a patient. Also, current methods
and tools used to predict patients at risk of admission fail to deal with
uncertainty in unplanned admission, LOS, patients- characteristics.
The main objective of this paper is to deal with uncertainty in
health system variables, and handles uncertain relationship among
variables. An introduction of machine learning techniques along with
statistical methods like Regression methods can be a proposed
solution approach to handle uncertainty in health system variables. A
model that adapts fuzzy methods to handle uncertain data and
uncertain relationships can be an efficient solution to capture the
vague definition of admission of a patient.
Food Quality Labels and their Perception by Consumers in the Czech Republic
The paper deals with quality labels used in the food products market, especially with labels of quality, labels of origin, and labels of organic farming. The aim of the paper is to identify perception of these labels by consumers in the Czech Republic. The first part refers to the definition and specification of food quality labels that are relevant in the Czech Republic. The second part includes the discussion of marketing research results. Data were collected with personal questioning method. Empirical findings on 150 respondents are related to consumer awareness and perception of national and European food quality labels used in the Czech Republic, attitudes to purchases of labelled products, and interest in information regarding the labels. Statistical methods, in the concrete Pearson´s chi-square test of independence, coefficient of contingency, and coefficient of association are used to determinate if significant differences do exist among selected demographic categories of Czech consumers.
Investigation of Genetic Epidemiology of
Metabolic Compromises in ß Thalassemia Minor
Mutation: Phenotypic Pleiotropy
Human genome is not only the evolutionary
summation of all advantageous events, but also houses lesions of
deleterious foot prints. A single gene mutation sometimes may
express multiple consequences in numerous tissues and a linear
relationship of the genotype and the phenotype may often be obscure.
ß Thalassemia minor, a transfusion independent mild anaemia,
coupled with environment among other factors may articulate into
phenotypic pleotropy with Hypocholesterolemia, Vitamin D
deficiency, Tissue hypoxia, Hyper-parathyroidism and Psychological
alterations. Occurrence of Pancreatic insufficiency, resultant
steatorrhoea, Vitamin-D (25-OH) deficiency (13.86 ngm/ml) with
Hypocholesterolemia (85mg/dl) in a 30 years old male ß Thal-minor
patient (Hemoglobin 11mg/dl with Fetal Hemoglobin 2.10%, Hb A2
4.60% and Hb Adult 84.80% and altered Hemogram) with increased
Para thyroid hormone (62 pg/ml) & moderate Serum Ca+2
(9.5mg/ml) indicate towards a cascade of phenotypic pleotropy
where the ß Thalassemia mutation ,be it in the 5’ cap site of the
mRNA , differential splicing etc in heterozygous state is effecting
several metabolic pathways. Compensatory extramedulary
hematopoiesis may not coped up well with the stressful life style of
the young individual and increased erythropoietic stress with high
demand for cholesterol for RBC membrane synthesis may have
resulted in Hypocholesterolemia.Oxidative stress and tissue hypoxia
may have caused the pancreatic insufficiency, leading to Vitamin D
deficiency. This may in turn have caused the secondary
hyperparathyroidism to sustain serum Calcium level. Irritability and
stress intolerance of the patient was a cumulative effect of the vicious
cycle of metabolic compromises. From these findings we propose
that the metabolic deficiencies in the ß Thalassemia mutations may
be considered as the phenotypic display of the pleotropy to explain
the genetic epidemiology.
According to the recommendations from the NIH Workshop on
Gene-Environment Interplay in Common Complex Diseases: Forging
an Integrative Model, study design of observations should be
informed by gene-environment hypotheses and results of a study
(genetic diseases) should be published to inform future hypotheses.
Variety of approaches is needed to capture data on all possible
aspects, each of which is likely to contribute to the etiology of
disease. Speakers also agreed that there is a need for development of
new statistical methods and measurement tools to appraise
information that may be missed out by conventional method where
large sample size is needed to segregate considerable effect.
A meta analytic cohort study in future may bring about significant
insight on to the title comment.
Fault Detection of Pipeline in Water Distribution Network System
Water pipe network is installed underground and once equipped, it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed
after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and
minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate
or pressure. The transient model describing water flow in pipelines
is presented and simulated using MATLAB. The fault situations such
as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using
statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the
better fault detection performance.