Kajian yang dijalankan adalah berkaitan dengan penentuan model yang sesuai serta analisis data penyerapan logam berat oleh sayuran berdaun yang terpilih iaitu kangkung (Ipomea aquatica), sawi bunga (Brassica chinensis var parachinensis), bayam (Amaranthus oleraceus L) dan sawi putih (Brassica chinensis L.). Kajian ini bertujuan untuk menentukan dan membandingkan kandungan serta corak pengambilan logam berat yang diserap oleh sayuran dan juga bahagian-bahagiannya yang meliputi daun, batang dan akar. Penentuan model yang dibuat bertujuan bagi melihat corak penyerapan logam berat oleh sayuran atau bahagian sayuran tertentu. Logam berat yang dikaji terdiri daripada kadmium , kromium, kuprum, ferum , mangan, plumbum dan zink. Plot serakan digunakan bagi menentukan corak pengambilan logam berat dalam sayuran dan bahagian-bahagiannya. Selain itu ujian Kruskal-Wallis digunakan bagi membuat perbandingan median di antara logam berat yang diserap oleh sayuran yang dikaji. Nilai khi-kuasa dua dan juga nilai-p digunakan bagi menentukan sama ada sesuatu logam berat yang diserap itu berkait rapat dengan jenis sayuran secara signifikan. Secara umum bolehlah dikatakan bahawa logam Fe, Mn dan Zn adalah dominan dalam semua bahagian sayuran yang dikaji. Selain itu, melalui ujian Kruskal-Wallis didapati penyerapan kesemua logam berat pada setiap bahagian sayuran adalah berbeza secara signifikan. Penyuaian model regresi linear, kuadratik, kubik atau eksponen telah dilakukan terhadap data ini dan didapati kebanyakan data dapat disuaikan dengan baik oleh model kuadratik dan kubik berdasarkan nilai pekali penentuan (R2).
This paper gives a comprehensive discussion on complex regression model by extending the idea of regression model to circular variables. Various aspect have been considered such as the biasness of parameters, error assumptions and model checking. The advantage of this approach is that it allows the use of usual technique available in ordinary linear regression for the regression of circular variables. The quality of the estimates and the feasibility of the approach were illustrated via simulation. The model was then applied to the wave direction data.
Estimation and forecast of groundwater recharge and capacity of aquifer are essential issues in water resources investigation. In the current research, groundwater recharge, recharge coefficient and effective rainfall were determined through a case study using empirical methods applicable to the tropical zones. The related climatological data between January 2000 and December 2010 were collected in Selangor, Malaysia. The results showed that groundwater recharge was326.39 mm per year, effective precipitation was 1807.97 mm per year and recharge coefficient was 18% for the study area. In summary, the precipitation converted to recharge, surface runoff and evapotranspiration are 12, 32 and 56% of rainfall, respectively. Correlation between climatic parameters and groundwater recharge showed positive and negative relationships. The highest correlation was found between precipitation and recharge. Linear multiple regressions between
recharge and measured climatologic data proved significant relationship between recharge and rainfall and wind speed. It was also proven that the proposed model provided an accurate estimation for similar projects.
This paper investigates a novel offset-free control scheme based on a multiple model predictive controller (MMPC) and an adaptive integral action controller for nonlinear processes. Firstly, the multiple model description captures the essence of the nonlinear process, while keeping the MPC optimization linear. Multiple models also enable the controller to deal with the uncertainty associated with changing setpoint. Then, a min-max approach is utilized to counter the effect of parametric uncertainty between the linear models and the nonlinear process. Finally, to deal with other uncertainties, such as input and output disturbances, an adaptive integral action controller is run in parallel to the MMPC. Thus creating a novel offset-free approach for nonlinear systems that is more easily tuned than observer-based MPC. Simulation results for a pH-controller, which acts as an example of a nonlinear process, are presented to demonstrate the usefulness of the technique compared to using an observer-based MPC.
Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a
data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the
forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear
relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good
model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this
combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance
of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the
error graphically. From the result obtained this model gives a better forecast compare to the other two models.
Achieving reliable power efficiency from a high voltage induction motor (HVIM) is a great challenge, as the rigorous control strategy is susceptible to unexpected failure. External cooling is commonly used in an HVIM cooling system, and it is a vital part of the motor that is responsible for keeping the motor at the proper operating temperature. A malfunctioning cooling system component can cause motor overheating, which can destroy the motor and cause the entire plant to shut down. As a result, creating a dynamic model of the motor cooling system for quality performance, failure diagnosis, and prediction is critical. However, the external motor cooling system design in HVIM is limited and separately done in the past. With this issue in mind, this paper proposes a combined modeling approach to the HVIM cooling system which consists of integrating the electrical, thermal, and cooler model using the mathematical model for thermal performance improvement. Firstly, the development of an electrical model using an established mathematical model. Subsequently, the development of a thermal model using combined mathematical and linear regression models to produce motor temperature. Then, a modified cooler model is developed to provide cold air temperature for cooling monitoring. All validated models are integrated into a single model called the HVIM cooling system as the actual setup of the HVIM. Ultimately, the core of this modeling approach is integrating all models to accurately represent the actual signals of the motor cooler temperature. Then, the actual signals are used to validate the whole structure of the model using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) analysis. The results demonstrate the high accuracy of the HVIM cooling system representation with less than 1% error tolerance based on the industrial plant experts. Thus, it will be helpful for future utilization in quality maintenance, fault identification and prediction study.
This study presented a novel methodology to predict microalgae chlorophyll content from colour models using linear regression and artificial neural network. The analysis was performed using SPSS software. Type of extractant solvents and image indexes were used as the input data for the artificial neural network calculation. The findings revealed that the regression model was highly significant, with high R2 of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R2 of 0.66 and low RMSE of 2.36 proved to be more accurate than regression model. The model which fitted to the experimental data indicated that acetone was a suitable extraction solvent. In comparison to the cyan-magenta-yellow-black model in image analysis, the red-greenblue model offered a better correlation. In short, the estimation of chlorophyll concentration using prediction models are rapid, more efficient, and less expensive.
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.
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.
Awareness of haze pollution and management increased in Southeast Asia since 1990. However, the
focus on environmental management is decreasing especially in Malaysia due to the abundant
resources and increased development pressure. The total health damage cost because of haze in the
country became significantly high due to the long duration of haze events year by year. This paper
discusses the health damage caused by bronchitis due to the haze events in Malaysia. The analysis
shows positive coefficient of independent variables which indicates the positive relationship between
dependent variable and independent variables. Multiple linear regression analysis shows that 45.3%
variation in damage cost of bronchitis could be explained by FAI, GDPPC, and CO2.
This work investigates a new interrogation method of a fiber Bragg grating (FBG) sensor based on longer and shorter wavelengths to distinguish between transversal forces and temperature variations. Calibration experiments were carried out to examine the sensor's repeatability in response to the transversal forces and temperature changes. An automated calibration system was developed for the sensor's characterization, calibration, and repeatability testing. Experimental results showed that the FBG sensor can provide sensor repeatability of 13.21 pm and 17.015 pm for longer and shorter wavelengths, respectively. The obtained calibration coefficients expressed in the linear model using the matrix enabled the sensor to provide accurate predictions for both measurements. Analysis of the calibration and experiment results implied improvements for future work. Overall, the new interrogation method demonstrated the potential to employ the FBG sensing technique where discrimination between two/three measurands is needed.
Background: The ability to adapt to the psychosocial disruptions associated with the refugee experience may influence the course of complicated grief reactions. Objective: We examine these relationships amongst Myanmar refugees relocated to Malaysia who participated in a six-week course of Integrative Adapt Therapy (IAT). Method: Participants (n = 170) included Rohingya, Chin, and Kachin refugees relocated to Malaysia. At baseline and six-week post-treatment, we applied culturally adapted measures to assess symptoms of Prolonged Complex Bereavement Disorder (PCBD) and adaptive capacity to psychosocial disruptions, based on the Adaptive Stress Index (ASI). The ASI comprises five sub-scales of safety/security (ASI-1); bonds and networks (ASI-2); injustice (ASI-3); roles and identity (ASI-4); and existential meaning (ASI-5). Results: Multilevel linear models indicated that the relationship between baseline and posttreatment PCBD symptoms was mediated by the ASI scale scores. Further, ASI scale scores assessed posttreatment mediated the relationship between baseline and posttreatment PCBD symptoms. Mediation of PCBD change was greatest for the ASI II scale representing disrupted bonds and networks. Conclusion: Our findings are consistent with the informing model of IAT in demonstrating that changes in adaptive capacity, and especially in dealing with disrupted bonds and networks, may mediate the process of symptom improvement over the course of therapy.
Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data were collected from the year 1980 until 2014 from the website of the Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved using the least square method. Since least square problem is an unconstrained optimisation, the Conjugate Gradient (CG) was chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables. Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value.
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.
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.
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 application of solar disinfection for treating stored rainwater was investigated by the authors using indicator organisms. The multiple tube fermentation technique and pour plate method were used for the detection of microbial quality indicators like total and fecal coliforms, E. coli and heterotrophic plate count. These techniques have disadvantages mainly that these are laborious and time consuming. The correlation of total coliform with that of exposure time is proposed under different factors of weather, pH and turbidity. Statistical tools like root mean square error and coefficient of determination were used to validate these proposed equations. The correlation equations of fecal coliform, E. coli and heterotrophic plate count with total coliform are suggested by using four regression analysis including Reciprocal Quadratic, Polynomial Regression (2 degree), Gaussian Model and Linear Regression in order to reduce the tedious experimental work in similar types of experiments and treatment systems.