Abstract: In this paper, a model is proposed to determine the life
distribution parameters of the useful life region for the PV system
utilizing a combination of non-parametric and linear regression
analysis for the failure data of these systems. Results showed that this
method is dependable for analyzing failure time data for such reliable
systems when the data is scarce.
Abstract: Prosperity of electronic equipment in photocopying
environment not only has improved work efficiency, but also has
changed indoor air quality. Considering the number of photocopying
employed, indoor air quality might be worse than in general office
environments. Determining the contribution from any type of
equipment to indoor air pollution is a complex matter. Non-methane
hydrocarbons are known to have an important role on air quality due
to their high reactivity. The presence of hazardous pollutants in
indoor air has been detected in one photocopying shop in Novi Sad,
Serbia. Air samples were collected and analyzed for five days, during
8-hr working time in three time intervals, whereas three different
sampling points were determined. Using multiple linear regression
model and software package STATISTICA 10 the concentrations of
occupational hazards and microclimates parameters were mutually
correlated. Based on the obtained multiple coefficients of
determination (0.3751, 0.2389 and 0.1975), a weak positive
correlation between the observed variables was determined. Small
values of parameter F indicated that there was no statistically
significant difference between the concentration levels of nonmethane
hydrocarbons and microclimates parameters. The results
showed that variable could be presented by the general regression
model: y = b0 + b1xi1+ b2xi2. Obtained regression equations allow to
measure the quantitative agreement between the variables and thus
obtain more accurate knowledge of their mutual relations.
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: 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: 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: Our goal is development of an algorithm capable of
predicting the directional trend of the Standard and Poor’s 500 index
(S&P 500). Extensive research has been published attempting to
predict different financial markets using historical data testing on an
in-sample and trend basis, with many authors employing excessively
complex mathematical techniques. In reviewing and evaluating these
in-sample methodologies, it became evident that this approach was
unable to achieve sufficiently reliable prediction performance for
commercial exploitation. For these reasons, we moved to an out-ofsample
strategy based on linear regression analysis of an extensive
set of financial data correlated with historical closing prices of the
S&P 500. We are pleased to report a directional trend accuracy of
greater than 55% for tomorrow (t+1) in predicting the S&P 500.
Abstract: Many organizations bring e-Learning to use as a tool
in their training and human development department. It is getting
more popular because it is easy to access to get knowledge all the
time and also it provides a rich content, which can develop the
employees’ skill efficiently. This study is focused on the factors that
affect using e-Learning efficiently, so it will make job satisfaction
increasing. The questionnaires were sent to employees in large
commercial banks, which use e-Learning located in Bangkok, the
results from multiple linear regression analysis showed that
employee’s characteristics, characteristics of e-Learning, learning and
growth have influence on job satisfaction.
Abstract: Geopolymer concretes are new class of construction
materials that have emerged as an alternative to Ordinary Portland
cement concrete. Considerable researches have been carried out on
material development of geopolymer concrete; however, a few studies
have been reported on the structural use of them. This paper presents
the bond behaviors of reinforcement embedded in fly ash based
geopolymer concrete. The development lengths of reinforcement for
various compressive strengths of concrete, 20, 30 and 40 MPa, and
reinforcement diameters, 10, 16 and 25 mm, are investigated. Total 27
specimens were manufactured and pull-out test according to EN 10080
was applied to measure bond strength and slips between concrete and
reinforcements. The average bond strengths decreased from 23.06MPa
to 17.26 MPa, as the diameters of reinforcements increased from
10mm to 25mm. The compressive strength levels of geopolymer
concrete showed no significant influence on bond strengths in this
study. Also, the bond-slip relations between geopolymer concrete and
reinforcement are derived using non-linear regression analysis for
various experimental conditions.
Abstract: Stochastic User Equilibrium (SUE) model is a widely
used traffic assignment model in transportation planning, which is
regarded more advanced than Deterministic User Equilibrium (DUE)
model. However, a problem exists that the performance of the SUE
model depends on its error term parameter. The objective of this
paper is to propose a systematic method of determining the
appropriate error term parameter value for the SUE model. First, the
significance of the parameter is explored through a numerical
example. Second, the parameter calibration method is developed
based on the Logit-based route choice model. The calibration process
is realized through multiple nonlinear regression, using sequential
quadratic programming combined with least square method. Finally,
case analysis is conducted to demonstrate the application of the
calibration process and validate the better performance of the SUE
model calibrated by the proposed method compared to the SUE
models under other parameter values and the DUE model.
Abstract: Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
Abstract: This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.
Abstract: There is growing concern over increasing incidences of road accidents and consequent loss of human life in Qatar. In light to the future planned event in Qatar, World Cup 2022; Qatar should put into consideration the future deaths caused by road accidents, and past trends should be considered to give a reasonable picture of what may happen in the future. Qatar roads should be arranged and paved in a way that accommodate high capacity of the population in that time, since then there will be a huge number of visitors from the world. Qatar should also consider the risk issues of road accidents raised in that period, and plan to maintain high level to safety strategies. According to the increase in the number of road accidents in Qatar from 1995 until 2012, an analysis of elements affecting and causing road accidents will be effectively studied. This paper aims to identify and criticize the factors that have high effect on causing road accidents in the state of Qatar, and predict the total number of road accidents in Qatar 2022. Alternative methods are discussed and the most applicable ones according to the previous researches are selected for further studies. The methods that satisfy the existing case in Qatar were the multiple linear regression model (MLR) and artificial neutral network (ANN). Those methods are analyzed and their findings are compared. We conclude that by using MLR the number of accidents in 2022 will become 355,226 accidents, and by using ANN 216,264 accidents. We conclude that MLR gave better results than ANN because the artificial neutral network doesn’t fit data with large range varieties.
Abstract: Prognostic studies of the shoreline are of utmost importance for Ponta Negra Beach, located in Natal, Northeastern Brazil, where the infrastructure recently built along the shoreline is severely affected by flooding and erosion. This study compares shoreline predictions using three linear regression methods (LMS, LRR and WLR) and tries to discern the best method for different shoreline position scenarios. The methods have shown erosion on the beach in each of the scenarios tested, even in less intense dynamic conditions. The WLA_A with confidence interval of 95% was the well-adjusted model and calculated a retreat of -1.25 m/yr to -2.0 m/yr in hot spot areas. The change of the shoreline on Ponta Negra Beach can be measured as a negative exponential curve. Analysis of these methods has shown a correlation with the morphodynamic stage of the beach.
Abstract: The quality of video transmitted by mobile ad hoc networks (MANETs) can be influenced by several factors, including protocol layers; parameter settings of each protocol. In this paper, we are concerned with understanding the functional relationship between these influential factors and objective video quality in MANETs. We illustrate a systematic statistical design of experiments (DOE) strategy can be used to analyze MANET parameters and performance. Using a 2k factorial design, we quantify the main and interactive effects of 7 factors on a response metric (i.e., mean opinion score (MOS) calculated by PSNR with Evalvid package) we then develop a first-order linear regression model between the influential factors and the performance metric.
Abstract: The paper aims to compare the performance of vertical and inclined multiple plunging jets and to model and predict their mass transfer capacity by multi-linear regression based approach. The multiple vertical plunging jets have jet impact angle of θ = 90O; whereas, multiple inclined plunging jets have jet impact angle of θ = 60O. The results of the study suggests that mass transfer is higher for multiple jets, and inclined multiple plunging jets have up to 1.6 times higher mass transfer than vertical multiple plunging jets under similar conditions. The derived relationship, based on multi-linear regression approach, has successfully predicted the volumetric mass transfer coefficient (KLa) from operational parameters of multiple plunging jets with a correlation coefficient of 0.973, root mean square error of 0.002 and coefficient of determination of 0.946. The results suggests that predicted overall mass transfer coefficient is in good agreement with actual experimental values; thereby, suggesting the utility of derived relationship based on multi-linear regression based approach and can be successfully employed in modeling mass transfer by multiple plunging jets.
Abstract: Analyzing classroom assessments is one of the responsibilities of the teacher. It aims improving teacher’s instruction and assessment as well as student learning. The present study investigated factors that might explain variation in teachers’ practices regarding analysis of classroom assessments. The factors considered in the investigation included gender, in-service assessment training, teaching load, teaching experience, knowledge in assessment, attitude towards quantitative aspects of assessment, and self-perceived competence in analyzing assessments. Participants were 246 in-service teachers in Oman. Results of a stepwise multiple linear regression analysis revealed that self-perceived competence was the only significant factor explaining the variance in teachers’ analysis of assessments. Implications for research and practice are discussed.
Abstract: Radiation shielding is an obstacle in long duration space exploration. Boron Nitride Nanotubes (BNNTs) have attracted attention as an additive to radiation shielding material due to B10’s large neutron capture cross section. The B10 has an effective neutron capture cross section suitable for low energy neutrons ranging from 10-5 to 104 eV and hydrogen is effective at slowing down high energy neutrons. Hydrogenated BNNTs are potentially an ideal nanofiller for radiation shielding composites. We use Molecular Dynamics (MD) Simulation via Material Studios Accelrys 6.0 to model the Young’s Modulus of Hydrogenated BNNTs. An extrapolation technique was employed to determine the Young’s Modulus due to the deformation of the nanostructure at its theoretical density. A linear regression was used to extrapolate the data to the theoretical density of 2.62g/cm3. Simulation data shows that the hydrogenated BNNTs will experience a 11% decrease in the Young’s Modulus for (6,6) BNNTs and 8.5% decrease for (8,8) BNNTs compared to non-hydrogenated BNNT’s. Hydrogenated BNNTs are a viable option as a nanofiller for radiation shielding nanocomposite materials for long range and long duration space exploration.
Abstract: Extreme rainfall frequency analysis for Meteorological Sub-Division 4 of India was analyzed using L-moments approach. Serial Correlation and Mann Kendall tests were conducted for checking serially independent and stationarity of the observations. The discordancy measure for the sites was conducted to detect the discordant sites. The regional homogeneity was tested by comparing with 500 generated homogeneous regions using a 4 parameter Kappa distribution. The best fit distribution was selected based on ZDIST statistics and L-moments ratio diagram from the five extreme value distributions GPD, GLO, GEV, P3 and LP3. The LN3 distribution was selected and regional rainfall frequency relationship was established using index-rainfall procedure. A regional mean rainfall relationship was developed using multiple linear regression with latitude and longitude of the sites as variables.
Abstract: In this study, we analyzed the factors that affect research funds using linear regression analysis to increase the effectiveness of investments in national research projects. We collected 7,916 items of data on research projects that were in the process of being finished or were completed between 2010 and 2011. Data pre-processing and visualization were performed to derive statistically significant results. We identified factors that affected funding using analysis of fit distributions and estimated increasing or decreasing tendencies based on these factors.