Abstract: Analysis of the uncertainty quantification related to nuclear safety margins applied to the nuclear reactor is an important concept to prevent future radioactive accidents. The nuclear fuel performance code may involve the tolerance level determined by traditional deterministic models producing acceptable results at burn cycles under 62 GWd/MTU. The behavior of nuclear fuel can simulate applying a series of material properties under irradiation and physics models to calculate the safety limits. In this study, theoretical predictions of nuclear fuel failure under transient conditions investigate extended radiation cycles at 75 GWd/MTU, considering the behavior of fuel rods in light-water reactors under reactivity accident conditions. The fuel pellet can melt due to the quick increase of reactivity during a transient. Large power excursions in the reactor are the subject of interest bringing to a treatment that is known as the Fuchs-Hansen model. The point kinetic neutron equations show similar characteristics of non-linear differential equations. In this investigation, the multivariate logistic regression is employed to a probabilistic forecast of fuel failure. A comparison of computational simulation and experimental results was acceptable. The experiments carried out use the pre-irradiated fuels rods subjected to a rapid energy pulse which exhibits the same behavior during a nuclear accident. The propagation of uncertainty utilizes the Wilk's formulation. The variables chosen as essential to failure prediction were the fuel burnup, the applied peak power, the pulse width, the oxidation layer thickness, and the cladding type.
Abstract: Physical activity as a part of people’s everyday life reduces the risk of many diseases, including those induced by lifestyle, e.g. obesity, type 2 diabetes, osteoporosis, coronary heart disease, degenerative arthritis, and certain types of cancer. That refers particularly to professionally active people, including the early senior group working on non-manual positions. The aim of the study is to evaluate the relationship between physical activity and the socio-economic status of non-manual workers from Wroclaw—one of the biggest cities in Poland, a model setting for such investigations in this part of Europe. The crucial problem in the research is to find out the percentage of respondents who meet the health-related recommendations of the World Health Organization (WHO) concerning the volume, frequency, and intensity of physical activity, as well as to establish if the most important socio-economic factors, such as gender, age, education, marital status, per capita income, savings and debt, determine the compliance with the WHO physical activity recommendations. During the research, conducted in 2013, 1,170 people (611 women and 559 men) aged 21–60 years were examined. A diagnostic poll method was applied to collect the data. Physical activity was measured with the use of the short form of the International Physical Activity Questionnaire with extended socio-demographic questions, i.e. concerning gender, age, education, marital status, income, savings or debts. To evaluate the relationship between physical activity and selected socio-economic factors, logistic regression was used (odds ratio statistics). Statistical inference was conducted on the adopted ex ante probability level of p
Abstract: Neighbourhood environment walkability on reported physical activity (PA) levels of students of Universiti Sains Malaysia (USM) in Malaysia. Compared with previous generations, today’s young people spend less time playing outdoors and have lower participation rates in PA. Research suggests that negative perceptions of neighbourhood walkability may be a potential barrier to adolescents’ PA. The sample consisted of 200 USM students (to 24 years old) who live outside of the main campus and engage in PA in sport halls and sport fields of USM. The data were analysed using the t-test, binary logistic regression, and discriminant analysis techniques. The present study found that youth PA was affected by neighbourhood environment walkability factors, including neighbourhood infrastructures, neighbourhood safety (crime), and recreation facilities, as well as street characteristics and neighbourhood design variables such as facades of sidewalks, roadside trees, green spaces, and aesthetics. The finding also illustrated that active students were influenced by street connectivity, neighbourhood infrastructures, recreation facilities, facades of sidewalks, and aesthetics, whereas students in the less active group were affected by access to destinations, neighbourhood safety (crime), and roadside trees and green spaces for their PAs. These results report which factors of built environments have more effect on youth PA and they message to the public to create more awareness about the benefits of PA on youth health.
Abstract: Cervical dentinal hypersensitivity (CDH) affects 8-30% of adults and nearly 85% of perio-treated patients. Various treatment schemes have been applied for treating CDH, among them being fluoride application, laser irradiation, and, recently, bioglass. The purpose of this study was to investigate the influence of bioglass, copper-bromide (Cu-Br) laser irradiation and their combination on dentinal tubule occlusion as a potential dentinal hypersensitivity treatment for CDH. 45 human dentin surfaces were organized into three equal groups: group A received Cu-Br laser only; group B received bioglass only; group C received bioglass followed by Cu-Br laser irradiation. Specimens were evaluated with regard to dentinal tubule occlusion under environmental scanning electron microscope. Treatment modality significantly affected dentinal tubule occlusion (p
Abstract: Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates.
Abstract: Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.
Abstract: The under-5 mortality rate is high in sub-Saharan Africa with Lesotho being amongst the highest under-5 mortality rates in the world. The objective of the study is to determine the factors associated with under-5 mortality in Lesotho. The data used for this analysis come from the nationally representative household survey called the 2009 Lesotho Demographic and Health Survey. Odds ratios produced by the logistic regression models were used to measure the effect of each independent variable on the dependent variable. Female children were significantly 38% less likely to die than male children. Children who were breastfed for 13 to 18 months and those who were breastfed for more than 19 months were significantly less likely to die than those who were breastfed for 12 months or less. Furthermore, children of mothers who stayed in Quthing, Qacha’s Nek and Thaba Tseka ran the greatest risk of dying. The results suggested that: sex of child, type of birth, breastfeeding duration, district, source of energy and marital status were significant predictors of under-5 mortality, after correcting for all variables.
Abstract: We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.
Abstract: Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
Abstract: Objectives: To determine the nutritional status and
risk factors associated with women practicing geophagia in QwaQwa,
South Africa. Materials and Methods: An observational epidemiological study
design was adopted which included an exposed (geophagia) and nonexposed
(control) group. A food frequency questionnaire, anthropometric measurements and blood sampling were applied to
determine nutritional status of participants. Logistic regression
analysis was performed in order to identify factors that were likely to
be associated with the practice of geophagia. Results: The mean total energy intake for the geophagia group (G)
and control group (C) were 10324.31 ± 2755.00 kJ and 10763.94 ±
2556.30 kJ respectively. Both groups fell within the overweight
category according to the mean Body Mass Index (BMI) of each
group (G= 25.59 kg/m2; C= 25.14 kg/m2). The mean serum iron
levels of the geophagia group (6.929 μmol/l) were significantly lower
than that of the control group (13.75 μmol/l) (p = 0.000). Serum
transferrin (G=3.23g/l; C=2.7054g/l) and serum transferrin saturation
(G=8.05%; C=18.74%) levels also differed significantly between
groups (p=0.00). Factors that were associated with the practice of
geophagia included haemoglobin (Odds ratio (OR):14.50), serumiron
(OR: 9.80), serum-ferritin (OR: 3.75), serum-transferrin (OR:
6.92) and transferrin saturation (OR: 14.50). A significant negative
association (p=0.014) was found between women who were wageearners
and those who were not wage-earners and the practice of
geophagia (OR: 0.143; CI: 0.027; 0.755). These findings seem to
indicate that a permanent income may decrease the likelihood of
practising geophagia. Key Findings: Geophagia was confirmed to be a risk factor for
iron deficiency in this community. The significantly strong
association between geophagia and iron deficiency emphasizes the
importance of identifying the practice of geophagia in women,
especially during their child bearing years.
Abstract: This study, for its research subjects, uses patients who
had undergone total knee replacement surgery from the database of the
National Health Insurance Administration. Through the review of
literatures and the interviews with physicians, important factors are
selected after careful screening. Then using Cross Entropy Method,
Genetic Algorithm Logistic Regression, and Particle Swarm
Optimization, the weight of each factor is calculated and obtained. In
the meantime, Excel VBA and Case Based Reasoning are combined
and adopted to evaluate the system. Results show no significant
difference found through Genetic Algorithm Logistic Regression and
Particle Swarm Optimization with over 97% accuracy in both
methods. Both ROC areas are above 0.87. This study can provide
critical reference to medical personnel as clinical assessment to
effectively enhance medical care quality and efficiency, prevent
unnecessary waste, and provide practical advantages to resource
allocation to medical institutes.
Abstract: Hemoglobin (HB) indicates anemia level and by
extension may reflect the nutritional level and perhaps the immunity
of an individual. Some antiretroviral drugs like Zidovudine are
known to cause anemia in people living with HIV/AIDS (PLWHA).
A cross sectional study using demographic data and blood specimen
from 218 female commercial sex workers attending antiretroviral
therapy (ART) clinics was conducted between December, 2009 and
July, 2011 to assess the effect of zidovudine on hematologic, and
RNA viral load of female sex workers receiving antiretroviral
treatment in north western Nigeria. Anemia is a common and serious
complication of both HIV infection and its treatment. In the setting of
HIV infection, anemia has been associated with decreased quality of
life, functional status, and survival. Antiretroviral therapy,
particularly the highly active antiretroviral therapy (HAART), has
been associated with a decrease in the incidence and severity of
anemia in HIV-infected patients who have received a HAART
regimen for at least 1 year. In this study, result has shown that of the
218 patients, 26 with hemoglobin count between 5.1 – 10g/dl were
observed to have the highest viral load count of 300,000 –
350,000copies/ml. It was also observed that most patients (190) with
HB of 10.1 – 15.0g/dl had viral load count of 200,000 – 250,000
copies /ml. An inverse relationship therefore exists i.e. the lower the
hemoglobin level, the higher the viral load count even though the test
statistics did not show any significance between the two (P = 0.206).
This shows that multivariate logistic regression analysis
demonstrated that anemia was associated with a CD4 + cell count
below 50/μL, female sex workers with a viral load above 100,000
copies/mL, who use zidovudine.
Severe anemia was less prevalent in this study population than in
historical comparators; however, mild to moderate anemia rates
remain high. The study therefore recommends that hematological and
virologic parameters be monitored closely in patients receiving first
line ART regimen.
Abstract: Introduction: There are multiple social, individual and
cultural factors that influence an individual’s decision to adopt family
planning methods especially among non-users in patriarchal societies
like Pakistan. Non-users, if targeted efficiently, can contribute
significantly to country’s CPR. A research study showed that nonusers
if convinced to adopt lactational amenorrhea method can shift
to long term methods in future. Research shows that if non users are
targeted efficiently a 59% reduction in unintended pregnancies in
Saharan Africa and South-Central and South-East Asia is anticipated.
Methods: We did secondary data analysis on Pakistan
Demographic Heath Survey (2012-13) dataset. Use of contraception
(never-use/ever-use) was the outcome variable. At univariate level
Chi-square/Fisher Exact test was used to assess relationship of
baseline covariates with contraception use. Then variables to be
incorporated in the model were checked for multicollinearity,
confounding and interaction. Then binary logistic regression (with an
urban-rural stratification) was done to find relationship between
contraception use and baseline demographic and social variables.
Results: The multivariate analyses of the study showed that
younger women (≤ 29 years)were more prone to be never users as
compared to those who were >30 years and this trend was seen in
urban areas (AOR 1.92, CI 1.453-2.536) as well as rural areas (AOR
1.809, CI 1.421-2.303). While looking at regional variation, women
from urban Sindh (AOR 1.548, CI 1.142-2.099) and urban
Balochistan (AOR 2.403, CI 1.504-3.839) had more never users as
compared to other urban regions. Women in the rich wealth quintile
were more never users and this was seen both in urban and rural
localities (urban (AOR 1.106 CI .753-1.624); rural areas (AOR 1.162,
CI .887-1.524)) even though these were not statistically significant.
Women idealizing more children (>4) are more never users as
compared to those idealizing less children in both urban (AOR 1.854,
CI 1.275-2.697) and rural areas (AOR 2.101, CI 1.514-2.916).
Women who never lost a pregnancy were more inclined to be nonusers
in rural areas (AOR 1.394, CI 1.127-1.723) .Women familiar
with only traditional or no method had more never users in rural areas
(AOR 1.717, CI 1.127-1.723) but in urban areas it wasn’t significant.
Women unaware of Lady Health Worker’s presence in their area
were more never users especially in rural areas (AOR 1.276, CI
1.014-1.607). Women who did not visit any care provider were more
never users (urban (AOR 11.738, CI 9.112-15.121) rural areas (AOR
7.832, CI 6.243-9.826)).
Discussion/Conclusion: This study concluded that government,
policy makers and private sector family planning programs should
focus on the untapped pool of never users (younger women from underserved provinces, in higher wealth quintiles, who desire more
children.). We need to make sure to cover catchment areas where
there are less LHWs and less providers as ignorance to modern
methods and never been visited by an LHW are important
determinants of never use. This all is in sync with previous literate
from similar developing countries.
Abstract: This study analyzes the innovative orientation of the
Croatian entrepreneurs. Innovative orientation is represented by the
perceived extent to which an entrepreneur’s product or service or
technology is new, and no other businesses offer the same product.
The sample is extracted from the GEM Croatia Adult Population
Survey dataset for the years 2003-2013. We apply descriptive
statistics, t-test, Chi-square test and logistic regression. Findings
indicate that innovative orientations vary with personal, firm, meso
and macro level variables, and between different stages in
entrepreneurship process. Significant predictors are occupation of the
entrepreneurs, size of the firm and export aspiration for both early
stage and established entrepreneurs. In addition, fear of failure,
expecting to start a new business and seeing an entrepreneurial career
as a desirable choice are predictors of innovative orientation among
early stage entrepreneurs.
Abstract: Estimation of a proportion has many applications in
economics and social studies. A common application is the estimation
of the low income proportion, which gives the proportion of people
classified as poor into a population. In this paper, we present this
poverty indicator and propose to use the logistic regression estimator
for the problem of estimating the low income proportion. Various
sampling designs are presented. Assuming a real data set obtained
from the European Survey on Income and Living Conditions, Monte
Carlo simulation studies are carried out to analyze the empirical
performance of the logistic regression estimator under the various
sampling designs considered in this paper. Results derived from
Monte Carlo simulation studies indicate that the logistic regression
estimator can be more accurate than the customary estimator under
the various sampling designs considered in this paper. The stratified
sampling design can also provide more accurate results.
Abstract: The problem of estimating a proportion has important
applications in the field of economics, and in general, in many areas
such as social sciences. A common application in economics is
the estimation of the headcount index. In this paper, we define the
general headcount index as a proportion. Furthermore, we introduce
a new quantitative method for estimating the headcount index. In
particular, we suggest to use the logistic regression estimator for the
problem of estimating the headcount index. Assuming a real data set,
results derived from Monte Carlo simulation studies indicate that the
logistic regression estimator can be more accurate than the traditional
estimator of the headcount index.
Abstract: Precipitation forecast is important in avoid incident of natural disaster which can cause loss in involved area. This review paper involves three techniques from artificial intelligence namely logistic regression, decisions tree, and random forest which used in making precipitation forecast. These combination techniques through VAR model in finding advantages and strength for every technique in forecast process. Data contains variables from rain domain. Adaptation of artificial intelligence techniques involved on rain domain enables the process to be easier and systematic for precipitation forecast.
Abstract: International market expansion involves a strategic process of market entry decision through which a firm expands its operation from domestic to the international domain. Hence, entry timing choices require the needs to balance the early entry risks and the problems in losing opportunities as a result of late entry into a new market. Questionnaire surveys administered to 115 Malaysian construction firms operating in 51 countries worldwide have resulted in 39.1 percent response rate. Factor analysis was used to determine the most significant factors affecting entry timing choices of the firms to penetrate the international market. A logistic regression analysis used to examine the firms’ entry timing choices, indicates that the model has correctly classified 89.5 per cent of cases as late movers. The findings reveal that the most significant factor influencing the construction firms’ choices as late movers was the firm factor related to the firm’s international experience, resources, competencies and financing capacity. The study also offers valuable information to construction firms with intention to internationalize their businesses.
Abstract: This study proposes a novel recommender system that uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user’s preference. The proposed model consists of two steps. In the first step, this study uses logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. Then, this study combines the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. In the second step, this study uses the market basket analysis to extract association rules for co-purchased products. Finally, the system selects customers who have high likelihood to purchase products in each product group and recommends proper products from same or different product groups to them through above two steps. We test the usability of the proposed system by using prototype and real-world transaction and profile data. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The results also show that the proposed system may be useful in real-world online shopping store.
Abstract: Recent financial international scandals around the world have led to a number of investigations into the effectiveness of corporate governance practices and audit quality. Although evidence of corporate governance practices and audit quality exists from developed economies, very scanty studies have been conducted in Egypt where corporate governance is just evolving. Therefore, this study provides evidence on the effectiveness of corporate governance practices and audit quality from a developing country. The data for analysis are gathered from the top 50 most active companies in the Egyptian Stock Exchange, covering the three year period 2007-2009. Logistic regression was used in investigating the questions that were raised in the study. Findings from the study show that board independence; CEO duality and audit committees significantly have relationship with audit quality. The results also, indicate that institutional investor and managerial ownership have no significantly relationship with audit quality. Evidence also exist that size of the company; complexity and business leverage are important factors in audit quality for companies quoted on the Egypt Stock Exchange.