Abstract: The present study was carried out to investigate the
effect of alloying elements and thermo-mechanical treatment (TMT)
i.e. hot rolling and forging with different reduction ratios on the
hardness (HV) and impact toughness (J) of heat-treated low alloy
steels. An understanding of the combined effect of TMT and alloying
elements and by measuring hardness, impact toughness, resulting
from different heat treatment following TMT of the low alloy steels,
it is possible to determine which conditions yielded optimum
mechanical properties and high strength to weight ratio.
Experimental Correlations between hot work reduction ratio,
hardness and impact toughness for thermo-mechanically heat treated
low alloy steels are analyzed quantitatively, and both regression and
mathematical hardness and impact toughness models are developed.
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 attempts to identify the factors influencing
on women empowerment of rural area in Sri Lanka through micro
finance services. Data were collected from one hundred (100) rural
women involving self-employment activities through a questionnaire
using direct personal interviews. Judgment and Convenience Random
sampling technique was used to select the sample size from three
Divisional Secretariat divisions of Kandawalai, Poonakari and
Karachchi in Kilinochchi District. The factor analysis was performed
on fourteen (14) variables for screening and reducing the variables to
identify the influencing factors on empowerment. Multiple regression
analysis was used to identify the relationship between the three
empowerment factors and the impact of micro finance on overall
empowerment of rural women. The result of this study summarized
the variables into three factors namely decision making, freedom to
mobility and family support and which are positively associated with
empowerment. In addition to this the value of adjusted R2 is 0.248
indicates that all the variables extracted can be explained 24.8% of
the variation in the women empowerment through microfinance.
Independent variables of these three factors have positive correlation
with women empowerment as well as significant values at 5 percent
level.
Abstract: Moringa oleifera is a nutritious vegetable tree with
varieties of potential uses, as almost every part of the Moringa
oleifera tree can be used for food. This study was conducted in Oyo
State, Nigeria, to find out the level of acceptability of Moringa
oleifera diversified products among rural and urban dwellers.
Purposive sampling was used to select two local governments’ areas.
Stratified sampling technique was also used to select one community
each from rural and urban areas while snowball sampling technique
was used to select ten respondents each from the two communities,
making a total number of forty respondents. Data were analyzed
using frequencies, percentages, Chi-square, Pearson Product Moment
Correlation and regression analysis. Result from the study revealed
that majority of the respondents (80%) fell within the age range of
20-49 years and 55% of them were male, 55% were married, 70% of
them were Christians, 80% of them had tertiary education. The result
also showed that 85% were aware of the Moringa plant and (65%) of
them have consumed Moringa oleifera and the perception statements
on the benefits of Moringa oleifera indicated that (52.5%) of the
respondents rated Moringa oleifera to be favorable, most of them had
high acceptability for Moringa egusi soup, Moringa tea, Moringa pap
and yam pottage with Moringa. The result of the hypotheses testing
showed that there is a significant relationship between sex of the
respondents and acceptability of the diversified Moringa oleifera
products (x2=6.465, p = 0.011). There is also a significant
relationship between family size of the respondents level of
acceptability of the Moringa oleifera products (r = 0.327, p = 0.040).
Based on the level of acceptability of Moringa oleifera diversified
products; the plant is of great economic importance to the populace.
Therefore, there should be more public awareness through the media
to enlighten people on the beneficial effects of Moringa oleifera.
Abstract: The research of juice flavor forecasting has become
more important in China. Due to the fast economic growth in China,
many different kinds of juices have been introduced to the market. If a
beverage company can understand their customers’ preference well,
the juice can be served more attractive. Thus, this study intends to
introducing the basic theory and computing process of grapes juice
flavor forecasting based on support vector regression (SVR). Applying
SVR, BPN, and LR to forecast the flavor of grapes juice in real data
shows that SVR is more suitable and effective at predicting
performance.
Abstract: Ulexite (Na2O.2CaO.5B2O3.16H2O) is boron mineral
that is found in large quantities in the Turkey and world. In this
study, the dissolution of this mineral in the disodium hydrogen
phosphate solutions has been studied. Temperature, concentration,
stirring speed, solid liquid ratio and particle size were selected as
parameters. The experimental results were successfully correlated by
linear regression using Statistica program. Dissolution curves were
evaluated shrinking core models for solid-fluid systems. It was
observed that increase in the reaction temperature and decrease in the
solid/liquid ratio causes an increase the dissolution rate of ulexite.
The activation energy was found to be 63.4 kJ/mol. The leaching of
ulexite was controlled by chemical reaction.
Abstract: A total of 115 yeast strains isolated from local cassava
processing wastes were measured for crude protein content. Among
these strains, the strain MSY-2 possessed the highest protein
concentration (>3.5 mg protein/mL). By using molecular
identification tools, it was identified to be a strain of Pichia
kudriavzevii based on similarity of D1/D2 domain of 26S rDNA
region. In this study, to optimize the protein production by MSY-2
strain, Response Surface Methodology (RSM) was applied. The
tested parameters were the carbon content, nitrogen content, and
incubation time. Here, the value of regression coefficient (R2) =
0.7194 could be explained by the model which is high to support the
significance of the model. Under the optimal condition, the protein
content was produced up to 3.77 g per L of the culture and MSY-2
strain contains 66.8 g protein per 100 g of cell dry weight. These
results revealed the plausibility of applying the novel strain of yeast
in single-cell protein production.
Abstract: The Aptima® HIV-1 Quant Dx Assay is a fully
automated assay on the Panther system. It is based on Transcription-
Mediated Amplification and real time detection technologies. This
assay is intended for monitoring HIV-1 viral load in plasma
specimens and for the detection of HIV-1 in plasma and serum
specimens.
Nine-hundred and seventy nine specimens selected at random
from routine testing at St Thomas’ Hospital, London were
anonymised and used to compare the performance of the Aptima
HIV-1 Quant Dx assay and Roche COBAS® AmpliPrep/COBAS®
TaqMan® HIV-1 Test, v2.0. Two-hundred and thirty four specimens
gave quantitative HIV-1 viral load results in both assays. The
quantitative results reported by the Aptima Assay were comparable to
those reported by the Roche COBAS AmpliPrep/COBAS TaqMan
HIV-1 Test, v2.0 with a linear regression slope of 1.04 and an
intercept on -0.097.
The Aptima assay detected HIV-1 in more samples than the
COBAS assay. This was not due to lack of specificity of the Aptima
assay because this assay gave 99.83% specificity on testing plasma
specimens from 600 HIV-1 negative individuals. To understand the
reason for this higher detection rate a side-by-side comparison of low
level panels made from the HIV-1 3rd international standard
(NIBSC10/152) and clinical samples of various subtypes were tested
in both assays. The Aptima assay was more sensitive than the
COBAS assay.
The good sensitivity, specificity and agreement with other
commercial assays make the HIV-1 Quant Dx Assay appropriate for
both viral load monitoring and detection of HIV-1 infections.
Abstract: Rice straw is lignocellulosic biomass which can be utilized as substrate for the biogas production. However, due to the property and composition of rice straw, it is difficult to be degraded by hydrolysis enzymes. One of the pretreatment methods that modify such properties of lignocellulosic biomass is the application of lignocellulose-degrading microbial consortia. The aim of this study is to investigate the effect of microbial consortia to enhance biogas production. To select the high efficient consortium, cellulase enzymes were extracted and their activities were analyzed. The results suggested that microbial consortium culture obtained from cattle manure is the best candidate compared to decomposed wood and horse manure. A microbial consortium isolated from cattle manure was then mixed with anaerobic sludge and used as inoculum for biogas production. The optimal conditions for biogas production were investigated using response surface methodology (RSM). The tested parameters were the ratio of amount of microbial consortium isolated and amount of anaerobic sludge (MI:AS), substrate to inoculum ratio (S:I) and temperature. Here, the value of the regression coefficient R2 = 0.7661 could be explained by the model which is high to advocate the significance of the model. The highest cumulative biogas yield was 104.6 ml/g-rice straw at optimum ratio of MI:AS, ratio of S:I, and temperature of 2.5:1, 15:1 and 44°C respectively.
Abstract: Taiwan is a hyper endemic area for the Hepatitis B
virus (HBV). The estimated total number of HBsAg carriers in the
general population who are more than 20 years old is more than 3
million. Therefore, a case record review is conducted from January
2003 to June 2007 for all patients with a diagnosis of acute hepatitis
who were admitted to the Emergency Department (ED) of a
well-known teaching hospital. The cost for the use of medical
resources is defined as the total medical fee. In this study, principal
component analysis (PCA) is firstly employed to reduce the number of
dimensions. Support vector regression (SVR) and artificial neural
network (ANN) are then used to develop the forecasting model. A total
of 117 patients meet the inclusion criteria. 61% patients involved in
this study are hepatitis B related. The computational result shows that
the proposed PCA-SVR model has superior performance than other
compared algorithms. In conclusion, the Child-Pugh score and
echogram can both be used to predict the cost of medical resources for
patients with acute hepatitis in the ED.
Abstract: Gypsum (CaSO4.2H2O) is a mineral that is found in
large quantities in the Turkey and in the World. In this study, the
dissolution of this mineral in the diammonium hydrogen phosphate
solutions has been studied. The dissolution and dissolution kinetics of
gypsum in diammonium hydrogen phosphate solutions will be useful
for evaluating of solid wastes containing gypsum. Parameters such as
diammonium hydrogen phosphate concentration, temperature and
stirring speed affecting on the dissolution rate of the gypsum in
diammonium hydrogen phosphate solutions were investigated. In
experimental studies have researched effectiveness of the selected
parameters. The dissolution of gypsum were examined in two parts at
low and high temperatures. The experimental results were
successfully correlated by linear regression using Statistica program.
Dissolution curves were evaluated shrinking core models for solidfluid
systems. The activation energy was found to be 34.58 kJ/mol
and 44.45 kJ/mol for the low and the high temperatures. The
dissolution of gypsum was controlled by chemical reaction both low
temperatures and high temperatures.
Abstract: The arm length, hand length, hand breadth and middle
finger length of 1540 right-handed industrial workers of Haryana
state was used to assess the relationship between the upper limb
dimensions and stature. Initially, the data were analyzed using basic
univariate analysis and independent t-tests; then simple and multiple
linear regression models were used to estimate stature using SPSS
(version 17). There was a positive correlation between upper limb
measurements (hand length, hand breadth, arm length and middle
finger length) and stature (p < 0.01), which was highest for hand
length. The accuracy of stature prediction ranged from ± 54.897 mm
to ± 58.307 mm. The use of multiple regression equations gave better
results than simple regression equations. This study provides new
forensic standards for stature estimation from the upper limb
measurements of male industrial workers of Haryana (India). The
results of this research indicate that stature can be determined using
hand dimensions with accuracy, when only upper limb is available
due to any reasons likewise explosions, train/plane crashes, mutilated
bodies, etc. The regression formula derived in this study will be
useful for anatomists, archaeologists, anthropologists, design
engineers and forensic scientists for fairly prediction of stature using
regression equations.
Abstract: An artificial neural network is a mathematical model
inspired by biological neural networks. There are several kinds of
neural networks and they are widely used in many areas, such as:
prediction, detection, and classification. Meanwhile, in day to day life,
people always have to make many difficult decisions. For example,
the coach of a soccer club has to decide which offensive player
to be selected to play in a certain game. This work describes a
novel Neural Network using a combination of the General Regression
Neural Network and the Probabilistic Neural Networks to help a
soccer coach make an informed decision.
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: In urban context, urban nodes such as amenity or
hazard will certainly affect house price, while classic hedonic analysis
will employ distance variables measured from each urban nodes.
However, effects from distances to facilities on house prices generally
do not represent the true price of the property. Distance variables
measured on the same surface are suffering a problem called
multicollinearity, which is usually presented as magnitude variance
and mean value in regression, errors caused by instability. In this paper,
we provided a theoretical framework to identify and gather the data
with less bias, and also provided specific sampling method on locating
the sample region to avoid the spatial multicollinerity problem in three
distance variable’s case.
Abstract: Data mining idea is mounting rapidly in admiration
and also in their popularity. The foremost aspire of data mining
method is to extract data from a huge data set into several forms that
could be comprehended for additional use. The data mining is a
technology that contains with rich potential resources which could be
supportive for industries and businesses that pay attention to collect
the necessary information of the data to discover their customer’s
performances. For extracting data there are several methods are
available such as Classification, Clustering, Association,
Discovering, and Visualization… etc., which has its individual and
diverse algorithms towards the effort to fit an appropriate model to
the data. STATISTICA mostly deals with excessive groups of data
that imposes vast rigorous computational constraints. These results
trials challenge cause the emergence of powerful STATISTICA Data
Mining technologies. In this survey an overview of the STATISTICA
software is illustrated along with their significant features.
Abstract: The objectives of the study were to determine the
marketing mix factors that influencing tourist’s destination decision
making for cultural tourism in the Chonburi province. Both
quantitative and qualitative data were applied in this study. The
samples of 400 cases for quantitative analysis were tourists (both
Thai and foreign) who were interested in cultural tourism in the
Chonburi province, and traveled to cultural sites in Chonburi and 14
representatives from provincial tourism committee of Chonburi and
local tourism experts. Statistics utilized in this research included
frequency, percentage, mean, standard deviation, and multiple
regression analysis. The study found that Thai and foreign tourists
are influenced by different important marketing mix factors. The
important factors for Thai respondents were physical evidence, price,
people, and place at high importance level. For foreign respondents,
physical evidence, price, people, and process were high importance
level, whereas, product, place and promotion were moderate
importance level.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: The development of allometric models is crucial to
accurate forest biomass/carbon stock assessment. The aim of this
study was to develop a set of biomass prediction models that will
enable the determination of total tree aboveground biomass for
savannah woodland area in Niger State, Nigeria. Based on the data
collected through biometric measurements of 1816 trees and
destructive sampling of 36 trees, five species specific and one site
specific models were developed. The sample size was distributed
equally between the five most dominant species in the study site
(Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa,
Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the
equations were developed for five individual species. Secondly these
five species were mixed and were used to develop an allometric
equation of mixed species. Overall, there was a strong positive
relationship between total tree biomass and the stem diameter. The
coefficient of determination (R2 values) ranging from 0.93 to 0.99 P
< 0.001 were realised for the models; with considerable low standard
error of the estimates (SEE) which confirms that the total tree above
ground biomass has a significant relationship with the dbh. F-test
values for the biomass prediction models were also significant at p
Abstract: Absorptive capacity generally facilitates the adoption
of innovation. How does this relationship change when economic
return is not the sole driver of innovation uptake? We investigate
whether absorptive capacity facilitates the adoption of green
innovation based on a survey of 79 construction companies in
Scotland. Based on the results of multiple regression analyses, we
confirm that existing knowledge utilisation (EKU), knowledge
building (KB) and external knowledge acquisition (EKA) are
significant predictors of green process GP), green administrative
(GA) and green technical innovation (GT), respectively. We discuss
the implications for theories of innovation adoption and knowledge
enhancement associated with environmentally-friendly practices.