Chitosan-tripolyphosphate (CTPP) beads were synthesized, characterized and were used for the adsorption of Pb(II) and Cu(II) ions from aqueous solution. The effects of initial pH, agitation period, adsorbent dosage, different initial concentrations of heavy metal ions and temperature were studied. The experimental data were correlated with the Langmuir, Freundlich and Dubinin-Radushkevich isotherm models. The maximum adsorption capacities of Pb(II) and Cu(II) ions in a single metal system based on the Langmuir isotherm model were 57.33 and 26.06 mg/g, respectively. However, the beads showed higher selectivity towards Cu(II) over Pb(II) ions in the binary metal system. Various thermodynamic parameters such as enthalpy (DeltaH degrees), Gibbs free energy (DeltaG degrees) and entropy (DeltaS degrees) changes were computed and the results showed that the adsorption of both heavy metal ions onto CTPP beads was spontaneous and endothermic in nature. The kinetic data were evaluated based on the pseudo-first and -second order kinetic and intraparticle diffusion models. Infrared spectra were used to elucidate the mechanism of Pb(II) and Cu(II) ions adsorption onto CTPP beads.
A depth-averaged numerical model was developed to study tidal circulation and suspended sediment transport in the gulf of Khambhat along the west coast of India. The spatial resolution of the model is 750m x 750m. A 2-D fine resolution (150 m x 150 m) model for the lower part of the Narmada estuary is coupled with the coarser gulf model to simulate the flow features in the lower estuary. The model dynamics and basic formulation remain the same for both the gulf model and the estuary model. The models are barotropic, based on the shallow water equations and neglect horizontal diffusion and wind stress terms in the momentum equations. The models are fully non-linear and use a semi-explicit finite difference scheme to solve mass, momentum, and advection- diffusion equation for suspended sediments in a horizontal plane. The erosion and deposition have been computed by an empirically developed source and sink term in the suspended sediment equation. The tide in the gulf is mainly represented in the model by the semi-diurnal M2 constituent. Meanwhile, fresh water discharge from the rivers joining the gulf had also been considered. Numerical experiments were carried out to study the circulation and suspended sediment concentrations in the gulf and estuarine region.
The paper presents a simulation work conducted on the elastomer subjected to cyclic loads. A 3D finite element model of elastomer specimen, in accordance to ASTM D412, was developed using CATIA and ANSYS commercial finite element (FEM) packages. Fatigue life predicted from the simulation was compared with well-documented published data and it showed an acceptable agreement. Meanwhile, the simulated strain-life results are slightly lower than the experimental data. Several factors which potentially influenced the variations of the results were noted. Finally, some recommendations are offered at the end of this study to further improve the simulation
An attempt was made in this investigation to trace the dynamic response of roller compacted concrete dam, which is subjected to horizontal ground motion by considering the interactions between flexible foundations, reservoir water, and bottom reservoir sediments. Two-dimensional finiteinfinite element was used for the non-linear elasto-plastic dynamic analysis. In this analysis, special emphasis was given to the non-linear behaviour of discontinuities along RCC dam-bedding rock foundation which was modelled by thin layer interface. Analysis was first carried out under static loading (self-weight and hydrostatic pressure), and this this was followed by seismic analysis, with hydrodynamic pressure effect in a dam-reservoir system. Based on the numerical dynamic results, it is concluded that the bottom reservoir sediment has significant effect on the seismic response of the RCC gravity dam. Moreover, there is a redistribution of the stresses at thin layer interface with significant stresses reduction, which is resulted from the release of energy through different modes of deformation in this region.
This paper presents investigations into the development of control schemes for end-point vibration
suppression and input tracking of a flexible manipulator. A constrained planar single-link flexible manipulator is considered and the dynamic model of the system is derived using the assumed mode method. To study the effectiveness of the controllers, a Linear Quadratic Regulator (LQR) was initially developed for control of rigid body motion. This is then extended to incorporate a noncollocated PID controller and a feedforward controller based on input shaping techniques to control vibration (flexible motion) of the system. For feedforward controller, positive and modified specified negative amplitude (SNA) input shapers are proposed and designed based on the properties of the system. Results from the simulation of the manipulator responses with the controllers are presented in time and frequency domains. The performances of the control schemes are assessed in terms of level of vibration reduction, input tracking capability and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed.
Natural ventilation is defined as the number of air exchanges per hour per unit floor area necessary
to reduce high indoor air temperature and humidity. In addition, it maintains the concentration of carbon dioxide. Natural ventilation is preferred in mechanical system as the ventilation opening is built into the greenhouse, with lower construction cost and no energy and maintenance inputs are required. A mathematical model to quantify natural ventilation rates was developed and verified in large-scale greenhouse structures. For this purpose, four Naturally Ventilated Tropical Greenhouse Structures were designed and constructed at the Malaysian Agricultural Research and Development Institute (MARDI). These were single, double, triple, and quadruple span structures with floor areas of 500 m2, 1000 m2, 1500 m2 and 2000 m2, respectively. This paper presents the validation of a mathematical model which was developed to quantify natural ventilation rates which are very crucial to reduce high in-house temperature built up in the tropics. Regression equations of natural ventilation against wind speed were found to be Φw = 0.0632V, Φw= 0.0395V, Φw= 0.0316Vand Φw=0.0276V for the single, double, triple and quadruple spans, respectively. Meanwhile, coefficients of determination showed strong relationships between ventilation rate and wind speed, with R2 = 0.9999 for all structures. Larger floor area was found to have higher in-house temperature than smaller ones. Ventilation rate inside the single-span structure was found to be higher compared to the multi-span structures, which increased linearly with the increasing wind speed at the eaves of structure.
Proper integrated management of a dam reservoir requires that all components of the water resource system be known. One of these components is the daily reservoir inflow which is the subject matter of this study, i.e. to establish predictions of what is coming in the next rainfall-runoff process over a catchment. The transformation of rainfall into runoff is an extremely complex, dynamic, and more of a non-linear process. The available six-year average daily rainfall data across the Sembrong dam catchment were computed using the well-known Theissen’s polygon method. Daily reservoir inflow data were extracted by applying the water balance model to the Sembrong dam reservoir. Modelling of relationship between rainfall and reservoir inflow data was done using feed-forward back-propagation neural networks. The final selected model has one hidden layer with 11 neurons in the hidden layer. The selected model was applied for an independent data series testing. Results in relation to specific climatic and hydrologic properties of a small tropical catchment suggested that the model is suitable to be used in forecasting the next day’s reservoir inflow. The efficiencies of the model Abtained indicated the validity of using the neural network for modelling reservoir inflow series.
Dielectric properties of natural rubber Hevea brasiliensis latex were measured at frequencies 0.2 to 20 GHz, at temperatures of 2, 15, 25, 35, and 50oC and around 30-98% moisture content. Measurements were done using open-ended coaxial line sensor and automated network analyzer. As expected, results showed that dielectric constant increased with increasing moisture. From 0.2 to 2.6 GHz, the losses were governed by conductive losses but for frequencies greater than 2.6 GHz, these were mainly due to dipolar losses. The former is due to conducting phases in hevea latex, while the latter is mainly governed by the orientation of water molecules. The results were analyzed at 2.6, 10, and 18 GHz, respectively. These were then compared with the values predicted by the dielectric mixture equations recommended by Weiner, Bruggeman and Kraszewski. All the measured values were found to be within the Weiner’s boundaries and close to the upper limit of Weiner’s model. It is also close to the predicted values of Bruggeman’s model with a/b = 0.1. All the models including Kraszewski are suitable for predicting the dielectric properties of hevea latex for frequencies 2.6 to 18 GHz, moisture content 30 to 98% and temperatures 2 to 50oC.
In this paper, a new method known as Grid Base Classifier was proposed. This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. On the other hand, the eager algorithms classify quickly, but they learn very slowly. The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. The method was developed based on the grid structure which was done to create a powerful method for classification. In the current research, the new algorithm was tested and applied to the multiclass classification of two or more categories, which are important for handling problems related to practical classification. The new method was also compared with the Levenberg-Marquardt back-propagation neural network in the learning stage and the Condensed nearest neighbour in the generalization stage to examine the performance of the model. The results from the artificial and real-world data sets (from UCI Repository) showed that the new method could improve both the efficiency and accuracy of pattern classification.
The purpose of this study is to measure and monitor the radon concentration from fabricated foamed light concrete, made of Portland cement, mine sand and granite. The concentration of radon released was measured using Radon Monitor Model 1027 from Sun Nuclear. The results of this research showed that the avearge radon concentration from foamed light concrete was 2.2 pCiL-1 L. Higher radon concentrations were detected after three days of measurements. Environment Protection Agency stated in its guidelines that radon concentration must lower than 4 pCiL-1 for a healthy environment. Thus, the use of foamed light concrete can be one of the alternatives to reduce radon concentration levels in human environment.
Hantaviruses are infectious agents that can cause diseases resulting in deaths in humans and are hosted by rodents without affecting the hosts themselves. A simple mathematical model describing the spread of the Hantavirus infection in rodents has been proposed and developed by Abramson and Kenkre where the model takes into account the temporal and spatial characteristics of this infection. In this paper, we extended this model to include the process of harvesting and study the impact of different harvesting strategies in the spread of the Hantavirus infection in rodents. Several numerical simulations were carried out and the results are discussed.
Hydrocarbon pollution in marine ecosystems occurs mainly by accidental oil spills, deliberate discharge of ballast waters from oil tankers and bilge waste discharges; causing site pollution and serious adverse effects on aquatic environments as well as human health. A large number of petroleum hydrocarbons are biodegradable, thus bioremediation has become an important method for the restoration of oil polluted areas. In this research, a series of natural attenuation, crude oil (CO) and dispersed crude oil (DCO) bioremediation experiments of artificially crude oil contaminated seawater was carried out. Bacterial consortiums were identified as Acinetobacter, Alcaligenes, Bacillus, Pseudomonas and Vibrio. First order kinetics described the biodegradation of crude oil. Under abiotic conditions, oil removal was 19.9% while a maximum of 31.8% total petroleum hydrocarbons (TPH) removal was obtained in natural attenuation experiment. All DCO bioreactors demonstrated higher and faster removal than CO bioreactors. Half life times were 28, 32, 38 and 58 days for DCO and 31, 40, 50 and 75 days for CO with oil concentrations of 100, 500, 1000 and 2000 mg/L, respectively. The effectiveness of Corexit 9500 dispersant was monitored in the 45 day study; the results indicated that it improved the crude oil biodegradation rate.
We implement a particle swarm optimization (PSO) algorithm to characterize stimulated Brillouin scattering phenomena in optical fibers. The explicit and strong dependence of the threshold exponential gain on the numerical aperture, the pump laser wavelength and the optical loss coefficient are presented. The proposed PSO model is also evaluated with the localized, nonfluctuating source model and the distributed (non-localized) fluctuating source model. Using our model, for fiber lengths from 1 km to 29 km, the calculated threshold exponential gain of stimulated Brillouin scattering is gradually decreased from 17.4 to 14.6 respectively. The theoretical results of Brillouin threshold power predicted by the proposed PSO model show a good agreement with the experimental results for different fiber lengths from 1 km to 12 km.
Candlenut oil was extracted using supercritical CO(2) (SC-CO(2)) with an optimization of parameters, by the response surface methodology. The ground candlenut samples were treated in 2 different ways, that is, dried in either a heat oven (sample moisture content of 2.91%) or dried in a vacuum oven (sample moisture content of 1.98%), before extraction. An untreated sample (moisture content of 4.87%) was used as a control. The maximum percentage of oil was extracted from the heat-oven-dried sample (77.27%), followed by the vacuum-oven-dried sample (74.32%), and the untreated sample (70.12%). At an SC-CO(2) pressure of 48.26 Mpa and 60 min of extraction time, the optimal temperatures for extraction were found to be 76.4 °C, 73.9 °C, and 70.6 °C for the untreated, heat-oven-dried, and vacuum-oven-dried samples, respectively. The heat-oven-dried sample contains the highest percentage of linoleic acid, followed by the untreated and vacuum-oven-dried samples. The antiradical activity of candlenut oil corresponded to an IC(50) value of 30.37 mg/mL.
Most highly motorized countries in the world have implemented different speed limits for light weight and heavy weight vehicles. The heavy vehicle speed limit is usually chosen to be lower than that of passenger cars due to the difficulty for the drivers to safely maneuver the heavy vehicle at high speed and greater impact during a crash. However, in many cases, the speed limit for heavy vehicle is set by only considering the vehicle size or category, mostly due to simplicity in enforcement. In this study, traffic and vehicular data for all vehicle types were collected using a weigh-in-motion system installed at Federal Route 54 in Malaysia. The first finding from the data showed that the weight variation for each vehicle category is considerable. Therefore, the effect of gross vehicle weight (GVW) and category of heavy vehicle on free flow speed and their interaction were analyzed using statistical techniques. Empirical analysis results showed that statistically for each type of heavy vehicle, there was a significant relationship between free flow speed of a heavy vehicle and GVW. Specifically, the results suggest that the mean and variance of free flow speed decrease with an increase GVW by the amount unrelated to size and shape for all GVW range. Then, based on the 85th percentile principle, the study proposed a new concept for setting the speed limit for heavy vehicle by incorporating GVW where a different speed limit is imposed to the heavy vehicle, not only based on vehicle classification, but also according to its GVW.
This paper assesses the potential equity impact of Malaysia's projected reform of its current tax financed system towards National Health Insurance (NHI).
The motivation behind this paper is to seek alternative techniques to achieve a near optimal controller for non-linear systems without solving the analytical problem. In classical optimal control systems, the system states and optimization co-state parameters generate a two-point boundary value problem (TPBVP) using Pontryagin's minimum principle (PMP). The paper contributes a new fuzzy time-optimal controller to the existing fuzzy controllers which has two regular inputs and one bang-bang output. The proposed controller closely approximates the output of the classical time-optimal controller. Further, input membership function are tuned on-line to improve the time-optimal output. The new controller exhibits optimal behaviour for second order non-linear systems. The rules are selected to satisfy the stability and optimality conditions of the new fuzzy time-optimal controller. The paper describes a systematic procedure to design the controller and how to achieve the desired result. To benchmark the new controller performance, a sliding mode controller is used for guidance and comparison purpose. Simulation of three non-linear examples shows promising results. The work described here is expected to incite researcher's interest in fuzzy time-optimal controller design.
The decomposition of municipal solid waste (MSW) in landfills under anaerobic conditions produces landfill gas (LFG) containing approximately 50-60% methane (CH(4)) and 30-40% carbon dioxide (CO(2)) by volume. CH(4) has a global warming potential 21 times greater than CO(2); thus, it poses a serious environmental problem. As landfills are the main method for waste disposal in Malaysia, the major aim of this study was to estimate the total CH(4) emissions from landfills in all Malaysian regions and states for the year 2009 using the IPCC, 1996 first-order decay (FOD) model focusing on clean development mechanism (CDM) project applications to initiate emission reductions. Furthermore, the authors attempted to assess, in quantitative terms, the amount of CH(4) that would be emitted from landfills in the period from 1981-2024 using the IPCC 2006 FOD model. The total CH(4) emission using the IPCC 1996 model was estimated to be 318.8 Gg in 2009. The Northern region had the highest CH(4) emission inventory, with 128.8 Gg, whereas the Borneo region had the lowest, with 24.2 Gg. It was estimated that Pulau Penang state produced the highest CH(4) emission, 77.6 Gg, followed by the remaining states with emission values ranging from 38.5 to 1.5 Gg. Based on the IPCC 1996 FOD model, the total Malaysian CH( 4) emission was forecast to be 397.7 Gg by 2020. The IPCC 2006 FOD model estimated a 201 Gg CH(4) emission in 2009, and estimates ranged from 98 Gg in 1981 to 263 Gg in 2024.
Early detection of a dengue outbreak is an important first step towards implementing effective dengue interventions resulting in reduced mortality and morbidity. A dengue mathematical model would be useful for the prediction of an outbreak and evaluation of control measures. However, such a model must be carefully parameterized and validated with epidemiological, ecological and entomological data. A field study was conducted to collect and analyse various parameters to model dengue transmission and outbreak. Dengue prone areas in Kuala Lumpur, Pahang, Kedah and Johor were chosen for this study. Ovitraps were placed outdoor and used to determine the effects of meteorological parameters on vector breeding. Vector population in each area was monitored weekly for 87 weeks. Weather stations, consisting of a temperature and relative humidity data logger and an automated rain gauge, were installed at key locations in each study site. Correlation and Autoregressive Distributed Lag (ADL) model were used to study the relationship among the variables. Previous week rainfall plays a significant role in increasing the mosquito population, followed by maximum humidity and temperature. The secondary data of rainfall, temperature and humidity provided by the meteorological department showed an insignificant relationship with the mosquito population compared to the primary data recorded by the researchers. A well fit model was obtained for each locality to be used as a predictive model to foretell possible outbreak.
A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372kg COD/(m(3)day)) and cyclic time (12, 24, and 48h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O&G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44kg COD/(m(3)day), TDS of 78,000mg/L and reaction time (RT) of 40h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100mg/L and met the discharge limits.