Replicated linear functional relationship model is often used to describe
relationships between two circular variables where both variables have error terms and
replicate observations are available. We derive the estimate of the rotation parameter
of the model using the maximum likelihood method. The performance of the proposed
method is studied through simulation, and it is found that the biasness of the estimates
is small, thus implying the suitability of the method. Practical application of the
method is illustrated by using a real data set.
In high-dimensional quantitative structure-activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.
Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
By using a linear operator, we obtain some new results for a normalized analytic function f defined by means of the Hadamard product of Hurwitz zeta function. A class related to this function will be introduced and the properties will be discussed.
Lycopene and total phenolics of pink guava puree industry by-products (refiner, siever and decanter)
were evaluated after steam blanching at selected temperatures and times. Lycopene content was in the order of decanter > siever > refiner (7.3, 6.3 and 1.5 mg/100 g, respectively), and the content of total phenolics was in the order of refiner > siever > decanter (4434.1, 2881.3 and 1529.3 mg GAE/100 g, respectively). Regression coefficients for temperatures (x1) and times (x2) from multiple linear regression models of siever and decanter showed significant (p
The increase in car usage due to economic prosperity has led to increase in occupant injuries. One way to reduce the injuries encountered by road accident victims is by implementing the rear seatbelt (RSB) law. Rear seatbelt wearing has been proven to save lives. In Malaysia, the implementation of the restraint system for front occupant has started in the 70's. However, the rear seatbelt enforcement law only came in 2009, after six months of an advocacy program. Prior to the introduction of the rear seatbelt law, rear seatbelt wearing rate was rather low, started to increase gradually during the advocacy period and jumped to the highest level after two month of the enforcement. This paper attempts to assess the effectiveness of the rear seatbelt intervention in reducing injuries among passenger car occupants in Malaysia using the generalized linear model (GLM). In GLM procedure, the dependent variable is the number of people from passenger vehicles that sustained severe and slight injuries, for the study period. The study period selected covers six months before implementation, six months during advocacy program, and six months after the law is implemented. The independent variables considered are enforcement and balik kampung activities (both are dummy variables) and time effect. Our results suggest that RSB intervention (p-value= 0.0001) had significantly reduced the number of people sustained serious and slight injuries by about 20%. The implementation of change in the RSB law has benefited not only in reducing the number of injuries but also result to great impact to the health outcomes.
Preliminary analysis of the short-term impact of a running headlights intervention revealed that there has been a significant drop in conspicuity-related motorcycle accidents in the pilot areas, Seremban and Shah Alam, Malaysia. This paper attempts to look in more detail at conspicuity-related accidents involving motorcycles. The aim of the analysis was to establish a statistical model to describe the relationship between the frequency of conspicuity-related motorcycle accidents and a range of explanatory variables so that new insights can be obtained into the effects of introducing a running headlight campaign and regulation. The exogenous variables in this analysis include the influence of time trends, changes in the recording and analysis system, the effect of fasting activities during Ramadhan and the "Balik Kampong" culture, a seasonal cultural-religious holiday activity unique to Malaysia. The model developed revealed that the running headlight intervention reduced the conspicuity-related motorcycle accidents by about 29%. It is concluded that the intervention has been successful in improving conspicuity-related motorcycle accidents in Malaysia.
Monthly data about oil production at several drilling wells is an example of
spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal
model, i.e. Feedforward Neural Network - VectorAutoregressive (FFNN-VAR) and FFNN
- Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast
accuracy to linearspatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal
models are proposed and applied for forecasting monthly oil production data at three
drilling wells in East Java, Indonesia. There are 60 observations that be divided to two
parts, i.e. the first 50 observations for training data and the last 10 observations for
testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear
spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11)
as linear spatio-temporal models. Moreover, further research about nonlinear spatiotemporal
models based on neural networks and GSTAR is needed for developing new
hybrid models that could improve the forecast accuracy.
This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.
Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO(2)) into ozone (O(3)) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O(3) in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O(3) concentration was tested. Results indicated that the presence of NO(2) and sunshine influence the concentration of O(3) in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.
This analysis demonstrates the application of a data duplication technique in linear regression with censored observations of the waiting time to third pregnancy ending in two outcome types, using data from Malaysia. The linear model not only confirmed the results obtained by the Cox proportional hazards model, but also identified two additional significant factors. The method provides a useful alternative when Cox proportionality assumption of the hazards is violated.
The intervals between pregnancies have important effects on fertility and maternal and infant health outcomes. This study uses linear regression with censored observation to assess the determinants of the waiting time to third pregnancy. The analysis is applied to data from the Second Malaysian Family Life Survey consisting of 1172 women who had their second delivery ending in a live birth. Contraceptive use, age of the woman, duration of breast-feeding, length of previous pregnancy interval and education of the woman all affect the waiting time to third pregnancy significantly.
The purpose of this study was to determine the adsorption coefficient (Koc) of chlorpyrifos in clay soil by measuring the Freundlich adsorption coefficient (Kads(f)) and desorption coefficient (1/n value) of chlorpyrifos. It was found that the Freundlich adsorption coefficient (Kads(f)) and the linear regression (r 2 ) of the Freundlich adsorption isotherm for chlorpyrifos in the clay soil were 52.6 L/kg and 0.5344, respectively. Adsoprtion equilibrium time was achieved within 24 hours for clay soil. This adsoprtion equilibrium time was used to determine the effect of concentration on adsorption. The adsorption coefficient (Koc) of clay soil was found to be 2783 L/kg with an initial concentration solution of 1 µg/g, soil-solution ratio (1:5) at 30 o C when the equilibrium between the soil matrix and solution was 24 hours. The Kdes decreased over four repetitions of the desorption process. The chlorpyrifos residues may be strongly adsorbed onto the surface of clay.
Studies have been carried out to determine the chemical (soluble solid content) and physical (firmness) parameters of locally grown Cavendish banana by near infrared (NIR) spectroscopy. NIR spectra in the wavelength region of 680-2500 nm were obtained from a total of 408 Cavendish bananas of different ripeness indices. Chemometrics using multiple linear regression (MLR) was applied to develop calibration models for prediction of firmness and soluble solid content (SSC) of Cavendish banana. Results showed that NIR spectroscopy had the feasibility for non-destructive determination of the quality of Cavendish banana. The coefficient of determination (R2) for firmness and SSC calibration models at different ripeness indices ranged from 0.78 to 0.86 and 0.75 to 0.96, respectively. The calibration models were validated using independent sets of data and prediction models developed with the root mean square error of prediction (RMSEP) ranged from 0.01 to 0.26 kgf and 0.039 to 0.788 Brix for firmness and SSC, respectively. The multi-index models showed considerable robustness but higher prediction error with RMSEP of 0.336 kgf for firmness and 0.937% Brix for SSC compared to index specific model.
Employees are an asset to an organisation where they could be the determinant behind organisational’s success or failure in an industry. In this study, the relationship between perceived organisational support (POS), perceived supervisor support (PSS), and organisational commitment (OC) with employee’s intention to stay with their current jobs were studied. For that purpose, 717 questionnaires were collected among casual dining restaurants employees in Klang Valley area and analyses Pearson correlation and multiple linear regression were run by using SPSS version 21. The results suggest that POS, PSS, and OC were positively correlated with employee’s intention to stay with their current job. Furthermore, OC was also found to be the most influential factor in affecting employees’ staying intention. The finding is hoped to have important implications where the management can formulate strategies to retain employees in restaurant industry in Malaysia.
The study is conducted to evaluate the significance of solar irradiance, ambient temperature and relative humidity as predictors and to quantify the relative contribution of these ambient parameters as predictors for photovoltaic module temperature model. The module temperature model was developed from experimental data of mono-crystalline and poly-crystalline PV modules retrofitted on metal roof in Klang Valley. The model was developed and analyzed using Multiple Linear Regressions (MLR) and Principle Component Analysis (PCA) Techniques. Solar irradiance, ambient temperature and relative humidity have been proven to be the significant predictors for module temperature. For poly-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 64.28 %, 17.45 % and 12.64 % respectively. For mono-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 66.12 %, 17.46 % and 12.48 % respectively. Thus, there is no significant difference in terms of relative contribution of these ambient parameters towards photovoltaic module temperature between poly-crystalline and mono-crystalline PV module technologies.
Observation of visible light trapping in zinc oxide (ZnO) nanorods (NRs) correlated to the optical and photoelectrochemical properties is reported. In this study, ZnO NR diameter and c-axis length respond primarily at two different regions, UV and visible light, respectively. ZnO NR diameter exhibits UV absorption where large ZnO NR diameter area increases light absorption ability leading to high efficient electron-hole pair separation. On the other hand, ZnO NR c-axis length has a dominant effect in visible light resulting from a multiphoton absorption mechanism due to light reflection and trapping behavior in the free space between adjacent ZnO NRs. Furthermore, oxygen vacancies and defects in ZnO NRs are associated with the broad visible emission band of different energy levels also highlighting the possibility of the multiphoton absorption mechanism. It is demonstrated that the minimum average of ZnO NR c-axis length must satisfy the linear regression model of Z p,min = 6.31d to initiate the multiphoton absorption mechanism under visible light. This work indicates the broadening of absorption spectrum from UV to visible light region by incorporating a controllable diameter and c-axis length on vertically aligned ZnO NRs, which is important in optimizing the design and functionality of electronic devices based on light absorption mechanism.
In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.
The fractal dimension is a global measure of complexity and is useful for quantifying anatomical structures, including the retinal vascular network. A previous study found a linear declining trend with aging on the retinal vascular fractal dimension (DF); however, it was limited to the older population (49 years and older). This study aimed to investigate the possible models of the fractal dimension changes from young to old subjects (10 to 73 years). A total of 215 right-eye retinal samples, including those of 119 (55%) women and 96 (45%) men, were selected. The retinal vessels were segmented using computer-assisted software, and non-vessel fragments were deleted. The fractal dimension was measured based on the log-log plot of the number of grids versus the size. The retinal vascular Df was analyzed to determine changes with increasing age. Finally, the data were fitted to three polynomial models. All three models are statistically significant (Linear: R(2) = 0.1270, 213 d.f., p<0.001, Quadratic: R(2) = 0.1536, 212 d.f., p<0.001, Cubic: R(2) = 0.1529, 211 d.f., p<0.001). The quadratic regression is significantly better than the linear regression (p<0.001); however, the increase in R(2) from the quadratic model to the cubic model is not significant (p=0.97). These results suggest that the decreasing trend of the fractal dimension associated with aging is better explained by the quadratic model than by the linear and cubic models in a sample with a broader age spectrum.
This study investigated relationships of a water quality index (WQI) with multiple water quality variables (WQVs), explored variability in water quality over time and space, and established linear and non-linear models predictive of WQI from raw WQVs. Data were processed using Spearman's rank correlation analysis, multiple linear regression, and artificial neural network modeling. Correlation analysis indicated that from a temporal perspective, the WQI, temperature, and zinc, arsenic, chemical oxygen demand, sodium, and dissolved oxygen concentrations increased, whereas turbidity and suspended solids, total solids, nitrate nitrogen (NO3-N), and biochemical oxygen demand concentrations decreased with year. From a spatial perspective, an increase with distance of the sampling station from the headwater was exhibited by 10 WQVs: magnesium, calcium, dissolved solids, electrical conductivity, temperature, NO3-N, arsenic, chloride, potassium, and sodium. At the same time, the WQI; Escherichia coli bacteria counts; and suspended solids, total solids, and dissolved oxygen concentrations decreased with distance from the headwater. Lastly, regression and artificial neural network models with high prediction powers (81.2% and 91.4%, respectively) were developed and are discussed.