Abstract: Quantitative Structure Activity Relationship (QSAR) analysis has been developed to relate antifungal activity of novel substituted 1,3,4-oxadiazole against Candida albicans and Aspergillus niger using computer assisted multiple regression analysis. The study has shown the better relationship between antifungal activities with respect to various descriptors established by multiple regression analysis. The analysis has shown statistically significant correlation with R2 values 0.932 and 0.782 against Candida albicans and Aspergillus niger respectively. These derivatives were further subjected to molecular docking studies to investigate the interactions between the target compounds and amino acid residues present in the active site of glucosamine-6-phosphate synthase. All the synthesized compounds have better docking score as compared to standard fluconazole. Our results could be used for the further design as well as development of optimal and potential antifungal agents.
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: Performances analysis of remote sensing sensor is required to pursue a range of scientific research and application objectives. Laboratory analysis of any remote sensing instrument is essential, but not sufficient to establish a valid inflight one. In this study, with the aid of the in situ measurements and corresponding image of three-gray scale permanent artificial target, the in-flight radiometric performances analyses (in-flight radiometric calibration, dynamic range and response linearity, signal-noise-ratio (SNR), radiometric resolution) of self-developed short-wave infrared (SWIR) camera are performed. To acquire the inflight calibration coefficients of the SWIR camera, the at-sensor radiances (Li) for the artificial targets are firstly simulated with in situ measurements (atmosphere parameter and spectral reflectance of the target) and viewing geometries using MODTRAN model. With these radiances and the corresponding digital numbers (DN) in the image, a straight line with a formulation of L = G × DN + B is fitted by a minimization regression method, and the fitted coefficients, G and B, are inflight calibration coefficients. And then the high point (LH) and the low point (LL) of dynamic range can be described as LH= (G × DNH + B) and LL= B, respectively, where DNH is equal to 2n − 1 (n is the quantization number of the payload). Meanwhile, the sensor’s response linearity (δ) is described as the correlation coefficient of the regressed line. The results show that the calibration coefficients (G and B) are 0.0083 W·sr−1m−2µm−1 and −3.5 W·sr−1m−2µm−1; the low point of dynamic range is −3.5 W·sr−1m−2µm−1 and the high point is 30.5 W·sr−1m−2µm−1; the response linearity is approximately 99%. Furthermore, a SNR normalization method is used to assess the sensor’s SNR, and the normalized SNR is about 59.6 when the mean value of radiance is equal to 11.0 W·sr−1m−2µm−1; subsequently, the radiometric resolution is calculated about 0.1845 W•sr-1m-2μm-1. Moreover, in order to validate the result, a comparison of the measured radiance with a radiative-transfer-code-predicted over four portable artificial targets with reflectance of 20%, 30%, 40%, 50% respectively, is performed. It is noted that relative error for the calibration is within 6.6%.
Abstract: Storm Event Analysis (SEA) provides a method to define rainfalls events as storms where each storm has its own amount and duration. By modelling daily probability of different types of storms, the onset, offset and cycle of rainfall seasons can be determined and investigated. Furthermore, researchers from the field of meteorology will be able to study the dynamical characteristics of rainfalls and make predictions for future reference. In this study, four categories of storms; short, intermediate, long and very long storms; are introduced based on the length of storm duration. Daily probability models of storms are built for these four categories of storms in Peninsular Malaysia. The models are constructed by using Bernoulli distribution and by applying linear regression on the first Fourier harmonic equation. From the models obtained, it is found that daily probability of storms at the Eastern part of Peninsular Malaysia shows a unimodal pattern with high probability of rain beginning at the end of the year and lasting until early the next year. This is very likely due to the Northeast monsoon season which occurs from November to March every year. Meanwhile, short and intermediate storms at other regions of Peninsular Malaysia experience a bimodal cycle due to the two inter-monsoon seasons. Overall, these models indicate that Peninsular Malaysia can be divided into four distinct regions based on the daily pattern for the probability of various storm events.
Abstract: Background: Sickle Cell Disease (SCD) is of major public-health concern globally, with majority of patients living in Africa. Despite its relevance, there is a dearth of research to determine the socio-demographic distribution and psychosocial impact of SCD in Africa. The objective of this study therefore was to examine the socio-demographic distribution and psychosocial consequences of SCD among patients in Ghana and to assess their quality of life and coping mechanisms. Methods: A cross-sectional research design was used, involving the completion of questionnaires on socio-demographic characteristics, quality of life of individuals, anxiety and depression. Participants were 387 male and female patients attending a sickle cell clinic in a public hospital. Results: Results showed no gender and marital status differences in anxiety and depression. However, there were age and level of education variances in depression but not in anxiety. In terms of quality of life, patients were more satisfied by the presence of love, friends, relatives as well as home, community and neighbourhood environment. While pains of varied nature and severity were the major reasons for attending hospital in SCD condition, going to the hospital as well as having Faith in God was the frequently reported mechanisms for coping with an unbearable SCD attacks. Multiple regression analysis showed that some socio-demographic and quality of life indicators had strong associations with anxiety and/or depression. Conclusion: It is recommended that a multi-dimensional intervention strategy incorporating psychosocial dimensions should be considered in the treatment and management of SCD.
Abstract: The identification and evaluation of organic and inorganic pollutants were performed in a flexographic facility in Novi Sad, Serbia. Air samples were collected and analyzed in situ, during 4-hours working time at five sampling points by the mobile gas chromatograph and ozonometer at the printing of collagen casing. Experimental results showed that the concentrations of isopropyl alcohol, acetone, total volatile organic compounds and ozone varied during the sampling times. The highest average concentrations of 94.80 ppm and 102.57 ppm were achieved at 200 minutes from starting the production for isopropyl alcohol and total volatile organic compounds, respectively. The mutual dependences between target hazardous and microclimate parameters were confirmed using a multiple linear regression model with software package STATISTICA 10. Obtained multiple coefficients of determination in the case of ozone and acetone (0.507 and 0.589) with microclimate parameters indicated a moderate correlation between the observed variables. However, a strong positive correlation was obtained for isopropyl alcohol and total volatile organic compounds (0.760 and 0.852) with microclimate parameters. Higher values of parameter F than Fcritical for all examined dependences indicated the existence of statistically significant difference between the concentration levels of target pollutants and microclimates parameters. Given that, the microclimate parameters significantly affect the emission of investigated gases and the application of eco-friendly materials in production process present a necessity.
Abstract: CO2 capture and storage technologies play a significant role in contributing to the control of climate change through the reduction of carbon dioxide emissions into the atmosphere. The present study evaluates and optimizes CO2 capture through a process, where carbon dioxide is passed into pH adjusted high salinity water and reacted with sodium chloride to form a precipitate of sodium bicarbonate. This process is based on a modified Solvay process with higher CO2 capture efficiency, higher sodium removal, and higher pH level without the use of ammonia. The process was tested in a bubble column semi-batch reactor and was optimized using response surface methodology (RSM). CO2 capture efficiency and sodium removal were optimized in terms of major operating parameters based on four levels and variables in Central Composite Design (CCD). The operating parameters were gas flow rate (0.5–1.5 L/min), reactor temperature (10 to 50 oC), buffer concentration (0.2-2.6%) and water salinity (25-197 g NaCl/L). The experimental data were fitted to a second-order polynomial using multiple regression and analyzed using analysis of variance (ANOVA). The optimum values of the selected variables were obtained using response optimizer. The optimum conditions were tested experimentally using desalination reject brine with salinity ranging from 65,000 to 75,000 mg/L. The CO2 capture efficiency in 180 min was 99% and the maximum sodium removal was 35%. The experimental and predicted values were within 95% confidence interval, which demonstrates that the developed model can successfully predict the capture efficiency and sodium removal using the modified Solvay method.
Abstract: The development of web technologies and mobile devices makes creating, accessing, using and sharing information or communicating with each other simpler every day. However, while the amount of information constantly increasing it is becoming harder to effectively organize and find quality information despite the availability of web search engines, filtering and indexing tools. Although digital technologies have overall positive impact on students’ lives, frequent use of these technologies and digital media enriched with dynamic hypertext and hypermedia content, as well as multitasking, distractions caused by notifications, calls or messages; can decrease the attention span, make thinking, memorizing and learning more difficult, which can lead to stress and mental exhaustion. This is referred to as “information overload”, “information glut” or “information anxiety”. Objective of this study is to determine whether students show signs of information overload and to identify the possible predictors. Research was conducted using a questionnaire developed for the purpose of this study. The results show that students frequently use technology (computers, gadgets and digital media), while they show moderate level of information literacy. They have sometimes experienced symptoms of information overload. According to the statistical analysis, higher frequency of technology use and lower level of information literacy are correlated with larger information overload. The multiple regression analysis has confirmed that the combination of these two independent variables has statistically significant predictive capacity for information overload. Therefore, the information science teachers should pay attention to improving the level of students’ information literacy and educate them about the risks of excessive technology use.
Abstract: This paper uses a primary data from 670 Chinese
manufacturing firms, together with the newly introduced regressionbased
inequality decomposition method, to study the effect of
openness on wage inequality. We find that openness leads to a
positive industry wage premium, but its contribution to firm-level
wage inequality is relatively small, only 4.69%. The major
contributor to wage inequality is human capital, which could explain
14.3% of wage inequality across sample firms.
Abstract: This research was aimed to investigate (1) the
relationship among local social movements, non-governmental
Organization activities and state measures deployment; and (2) the
effects of local social movements, non-governmental Organization
activities, and state measures deployment on conflict of local people
towards the Thai-Malaysian gas pipeline project. These people
included 1,000 residents of the four districts in Songkhla province.
The methods of data analysis consist of multiple regression analysis.
The results of the analysis showed that: (1) local social movements
depended on information, and mass communication; deployment of
state measures depended on compromise, coordination, and mass
communication; and (2) the conflict of local people depended on
mobilization, negotiation, and campaigning for participation of
people in the project. Thus, it is recommended that to successfully
implement any government policy, consideration must be paid to the
conflict of local people, mobilization, negotiation, and campaigning
for people’s participation in the project.
Abstract: Fuzzy regression models are useful for investigating
the relationship between explanatory variables and responses in fuzzy
environments. To overcome the deficiencies of previous models and
increase the explanatory power of fuzzy data, the graded mean
integration (GMI) representation is applied to determine
representative crisp regression coefficients. A fuzzy regression model
is constructed based on the modified dissemblance index (MDI),
which can precisely measure the actual total error. Compared with
previous studies based on the proposed MDI and distance criterion, the
results from commonly used test examples show that the proposed
fuzzy linear regression model has higher explanatory power and
forecasting accuracy.
Abstract: Forecasting electricity load plays a crucial role regards
decision making and planning for economical purposes. Besides, in
the light of the recent privatization and deregulation of the power
industry, the forecasting of future electricity load turned out to be a
very challenging problem. Empirical data about electricity load
highlights a clear seasonal behavior (higher load during the winter
season), which is partly due to climatic effects. We also emphasize
the presence of load periodicity at a weekly basis (electricity load is
usually lower on weekends or holidays) and at daily basis (electricity
load is clearly influenced by the hour). Finally, a long-term trend may
depend on the general economic situation (for example, industrial
production affects electricity load). All these features must be
captured by the model.
The purpose of this paper is then to build an hourly electricity load
model. The deterministic component of the model requires non-linear
regression and Fourier series while we will investigate the stochastic
component through econometrical tools.
The calibration of the parameters’ model will be performed by
using data coming from the Italian market in a 6 year period (2007-
2012). Then, we will perform a Monte Carlo simulation in order to
compare the simulated data respect to the real data (both in-sample
and out-of-sample inspection). The reliability of the model will be
deduced thanks to standard tests which highlight a good fitting of the
simulated values.
Abstract: Oil palm or Elaeis guineensis is considered as the golden crop in Malaysia. But oil palm industry in this country is now facing with the most devastating disease called as Ganoderma Basal Stem Rot disease. The objective of this paper is to analyze the economic loss due to this disease. There were three commercial oil palm sites selected for collecting the required data for economic analysis. Yield parameter used to measure the loss was the total weight of fresh fruit bunch in six months. The predictors include disease severity, change in disease severity, number of infected neighbor palms, age of palm, planting generation, topography, and first order interaction variables. The estimation model of yield loss was identified by using backward elimination based regression method. Diagnostic checking was conducted on the residual of the best yield loss model. The value of mean absolute percentage error (MAPE) was used to measure the forecast performance of the model. The best yield loss model was then used to estimate the economic loss by using the current monthly price of fresh fruit bunch at mill gate.
Abstract: It is not only the economic determinants that impact on life conditions, but maintaining a good level of life satisfaction (LS) may also be an important challenge currently. In Luxembourg, university students receive financial aid from the government. They are then registered at the Centre for Documentation and Information on Higher Education (CEDIES). Luxembourg is built on migration with almost half its population consisting of foreigners. It is upon this basis that our research aims to analyze the associations with mental health factors (health satisfaction, psychological quality of life, worry), perceived financial situation, career attitudes (adaptability, optimism, knowledge, planning) and LS, for non-Luxembourgish and native postgraduate students. Between 2012 and 2013, postgraduates registered at CEDIES were contacted by post and asked to participate in an online survey with either the option of English or French. The study population comprised of 644 respondents. Our statistical analysis excluded: those born abroad who had Luxembourgish citizenship, or those born in Luxembourg who did not have citizenship. Two groups were formed one consisting 147 non-Luxembourgish and the other 284 natives. A single item measured LS (1=not at all satisfied to 10=very satisfied). Bivariate tests, correlations and multiple linear regression models were used in which only significant relationships (p
Abstract: In Singapore, talent retention is one of the most persistent and real issue companies have to grapple with due to the tight labour market. Being resource-scarce, Singapore depends solely on its talented pool of high quality human resource to sustain its competitive advantage in the global economy. But the complex and multifaceted nature of turnover phenomenon makes the prescription of effective talent retention strategies in such a competitive labour market very challenging, especially when it comes to monetary incentives, companies struggle to answer the question of “How much is enough?” By examining the interactive effects of perceived alternative employment opportunities, annual salary and satisfaction with compensation on the turnover intention of 102 Singapore Professionals, Managers, Executives and Technicians (PMET) through correlation analyses and multiple regressions, important insights into the psyche of the Singapore talent pool can be drawn. It is found that annual salary influence turnover intention indirectly through mediation and moderation effects on PMET’s satisfaction on compensation. PMET are also found to be heavily swayed by better external opportunities. This implies that talent retention strategies should not adopt a purely monetary based blanket approach but rather a comprehensive and holistic one that considers the dynamics of prevailing market conditions.
Abstract: The current trends in affect recognition research are
to consider continuous observations from spontaneous natural
interactions in people using multiple feature modalities, and to
represent affect in terms of continuous dimensions, incorporate
spatio-temporal correlation among affect dimensions, and provide
fast affect predictions. These research efforts have been propelled
by a growing effort to develop affect recognition system that
can be implemented to enable seamless real-time human-computer
interaction in a wide variety of applications. Motivated by these
desired attributes of an affect recognition system, in this work
a multi-dimensional affect prediction approach is proposed by
integrating multivariate Relevance Vector Machine (MVRVM) with
a recently developed Output-associative Relevance Vector Machine
(OARVM) approach. The resulting approach can provide fast
continuous affect predictions by jointly modeling the multiple affect
dimensions and their correlations. Experiments on the RECOLA
database show that the proposed approach performs competitively
with the OARVM while providing faster predictions during testing.
Abstract: The present paper discusses the prediction of gas-liquid two-phase frictional pressure drop in a 2.12 mm horizontal circular minichannel using Artificial Neural Network (ANN). The experimental results are obtained with air as gas phase and water as liquid phase. The superficial gas velocity is kept in the range of 0.0236 m/s to 0.4722 m/s while the values of 0.0944 m/s, 0.1416 m/s and 0.1889 m/s are considered for superficial liquid velocity. The experimental results are predicted using different Artificial Neural Network (ANN) models. Networks used for prediction are radial basis, generalised regression, linear layer, cascade forward back propagation, feed forward back propagation, feed forward distributed time delay, layer recurrent, and Elman back propagation. Transfer functions used for networks are Linear (PURELIN), Logistic sigmoid (LOGSIG), tangent sigmoid (TANSIG) and Gaussian RBF. Combination of networks and transfer functions give different possible neural network models. These models are compared for Mean Absolute Relative Deviation (MARD) and Mean Relative Deviation (MRD) to identify the best predictive model of ANN.
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: The purposed of this research was to study the eating
habit of teenage pregnancy and its relationship to the knowledge of
nutrition during pregnancy. The 100 samples were derived from
simple random sampling technique of the teenage pregnancy in
Bangkae District. The questionnaire was used to collect data with the
reliability of 0.8. The data were analyzed by SPSS for Windows with
multiple regression technique. Percentage, mean and the relationship
of knowledge of eating and eating behavior were obtained. The
research results revealed that their knowledge in nutrition was at the
average of 4.07 and their eating habit that they mentioned most was
to refrain from alcohol and caffeine at 82% and the knowledge in
nutrition influenced their eating habits at 54% with the statistically
significant level of 0.001.
Abstract: Makishima and Mackenzie model was used to
simulation of acoustic properties (longitudinal and shear ultrasonic
wave velocities, elastic moduli theoretically for many tellurite and
borate glasses. The model was proposed mainly depending on the
values of the experimentally measured density, which are obtained
before. In this search work, we are trying to obtain the values of
densities of amorphous glasses (as the density depends on the
geometry of the network structure of these glasses). In addition, the
problem of simulating the slope of linear regression between the
experimentally determined bulk modulus and the product of packing
density and experimental Young's modulus, were solved in this
search work. The results showed good agreement between the
experimentally measured values of densities and both ultrasonic wave
velocities, and those theoretically determined.