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The Potential Cost Effectiveness of Different Dengue Vaccination Programmes in Malaysia: A Value-Based Pricing Assessment Using Dynamic Transmission Mathematical Modelling

Shafie AA, Yeo HY, Coudeville L, Steinberg L, Gill BS, Jahis R, et al.

Pharmacoeconomics, 2017 May;35(5):575-589.
PMID: 28205150 DOI: 10.1007/s40273-017-0487-3

BACKGROUND: Dengue disease poses a great economic burden in Malaysia.
METHODS: This study evaluated the cost effectiveness and impact of dengue vaccination in Malaysia from both provider and societal perspectives using a dynamic transmission mathematical model. The model incorporated sensitivity analyses, Malaysia-specific data, evidence from recent phase III studies and pooled efficacy and long-term safety data to refine the estimates from previous published studies. Unit costs were valued in $US, year 2013 values.
RESULTS: Six vaccination programmes employing a three-dose schedule were identified as the most likely programmes to be implemented. In all programmes, vaccination produced positive benefits expressed as reductions in dengue cases, dengue-related deaths, life-years lost, disability-adjusted life-years and dengue treatment costs. Instead of incremental cost-effectiveness ratios (ICERs), we evaluated the cost effectiveness of the programmes by calculating the threshold prices for a highly cost-effective strategy [ICER <1 × gross domestic product (GDP) per capita] and a cost-effective strategy (ICER between 1 and 3 × GDP per capita). We found that vaccination may be cost effective up to a price of $US32.39 for programme 6 (highly cost effective up to $US14.15) and up to a price of $US100.59 for programme 1 (highly cost effective up to $US47.96) from the provider perspective. The cost-effectiveness analysis is sensitive to under-reporting, vaccine protection duration and model time horizon.
CONCLUSION: Routine vaccination for a population aged 13 years with a catch-up cohort aged 14-30 years in targeted hotspot areas appears to be the best-value strategy among those investigated. Dengue vaccination is a potentially good investment if the purchaser can negotiate a price at or below the cost-effective threshold price.

Matched MeSH terms: Models, Theoretical*
Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill

Abunama T, Othman F, Ansari M, El-Shafie A

Environ Sci Pollut Res Int, 2019 Feb;26(4):3368-3381.
PMID: 30511225 DOI: 10.1007/s11356-018-3749-5

Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model's accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model-which applies two hidden layers-achieved the best performance, then followed by ANN-MLP1 model-which applies one hidden layer and three inputs-while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.

Matched MeSH terms: Models, Theoretical*
Fulltext How does exposure to pesticides vary in space and time for residents living near to treated orchards?

Wong HL, Garthwaite DG, Ramwell CT, Brown CD

Environ Sci Pollut Res Int, 2017 Dec;24(34):26444-26461.
PMID: 28948535 DOI: 10.1007/s11356-017-0064-5

This study investigated changes over 25 years (1987-2012) in pesticide usage in orchards in England and Wales and associated changes to exposure and risk for resident pregnant women living 100 and 1000 m downwind of treated areas. A model was developed to estimate aggregated daily exposure to pesticides via inhaled vapour and indirect dermal contact with contaminated ground, whilst risk was expressed as a hazard quotient (HQ) based on estimated exposure and the no observed (adverse) effect level for reproductive and developmental effects. Results show the largest changes occurred between 1987 and 1996 with total pesticide usage reduced by ca. 25%, exposure per unit of pesticide applied slightly increased, and a reduction in risk per unit exposure by factors of 1.3 to 3. Thereafter, there were no consistent changes in use between 1996 and 2012, with an increase in number of applications to each crop balanced by a decrease in average application rate. Exposure per unit of pesticide applied decreased consistently over this period such that values in 2012 for this metric were 48-65% of those in 1987, and there were further smaller decreases in risk per unit exposure. All aggregated hazard quotients were two to three orders of magnitude smaller than one, despite the inherent simplifications of assuming co-occurrence of exposure to all pesticides and additivity of effects. Hazard quotients at 1000 m were 5 to 16 times smaller than those at 100 m. There were clear signals of the impact of regulatory intervention in improving the fate and hazard profiles of pesticides used in orchards in England and Wales over the period investigated.

Matched MeSH terms: Models, Theoretical*
Digital image processing and analysis for activated sludge wastewater treatment

Khan MB, Lee XY, Nisar H, Ng CA, Yeap KH, Malik AS

Adv Exp Med Biol, 2015;823:227-48.
PMID: 25381111 DOI: 10.1007/978-3-319-10984-8_13

Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.

Matched MeSH terms: Models, Theoretical
A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system

Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS

Clin Respir J, 2016 Jul;10(4):486-94.
PMID: 25515741 DOI: 10.1111/crj.12250

BACKGROUND: Monitoring respiration is important in several medical applications. One such application is respiratory rate monitoring in patients with sleep apnoea. The respiratory rate in patients with sleep apnoea disorder is irregular compared with the controls. Respiratory phase detection is required for a proper monitoring of respiration in patients with sleep apnoea.
AIMS: To develop a model to detect the respiratory phases present in the pulmonary acoustic signals and to evaluate the performance of the model in detecting the respiratory phases.
METHODS: Normalised averaged power spectral density for each frame and change in normalised averaged power spectral density between the adjacent frames were fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance of both Mamdani and Sugeno methods, correlation coefficient and root mean square error (RMSE) were calculated.
RESULTS: In the correlation coefficient analysis in evaluating the fuzzy model using Mamdani and Sugeno method, the strength of the correlation was found to be r = 0.9892 and r = 0.9964, respectively. The RMSE for Mamdani and Sugeno methods are RMSE = 0.0853 and RMSE = 0.0817, respectively.
CONCLUSION: The correlation coefficient and the RMSE of the proposed fuzzy models in detecting the respiratory phases reveals that Sugeno method performs better compared with the Mamdani method.

Matched MeSH terms: Models, Theoretical
A bicycle safety index for evaluating urban street facilities

Asadi-Shekari Z, Moeinaddini M, Zaly Shah M

Traffic Inj Prev, 2015;16:283-8.
PMID: 24983474 DOI: 10.1080/15389588.2014.936010

The objectives of this research are to conceptualize the Bicycle Safety Index (BSI) that considers all parts of the street and to propose a universal guideline with microscale details.

Matched MeSH terms: Models, Theoretical
Fulltext Development of vision based multiview gait recognition system with MMUGait database

Ng H, Tan WH, Abdullah J, Tong HL

ScientificWorldJournal, 2014;2014:376569.
PMID: 25143972 DOI: 10.1155/2014/376569

This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.

Matched MeSH terms: Models, Theoretical
Multi-sources data fusion framework for remote triage prioritization in telehealth

Salman OH, Rasid MF, Saripan MI, Subramaniam SK

J Med Syst, 2014 Sep;38(9):103.
PMID: 25047520 DOI: 10.1007/s10916-014-0103-4

The healthcare industry is streamlining processes to offer more timely and effective services to all patients. Computerized software algorithm and smart devices can streamline the relation between users and doctors by providing more services inside the healthcare telemonitoring systems. This paper proposes a multi-sources framework to support advanced healthcare applications. The proposed framework named Multi Sources Healthcare Architecture (MSHA) considers multi-sources: sensors (ECG, SpO2 and Blood Pressure) and text-based inputs from wireless and pervasive devices of Wireless Body Area Network. The proposed framework is used to improve the healthcare scalability efficiency by enhancing the remote triaging and remote prioritization processes for the patients. The proposed framework is also used to provide intelligent services over telemonitoring healthcare services systems by using data fusion method and prioritization technique. As telemonitoring system consists of three tiers (Sensors/ sources, Base station and Server), the simulation of the MSHA algorithm in the base station is demonstrated in this paper. The achievement of a high level of accuracy in the prioritization and triaging patients remotely, is set to be our main goal. Meanwhile, the role of multi sources data fusion in the telemonitoring healthcare services systems has been demonstrated. In addition to that, we discuss how the proposed framework can be applied in a healthcare telemonitoring scenario. Simulation results, for different symptoms relate to different emergency levels of heart chronic diseases, demonstrate the superiority of our algorithm compared with conventional algorithms in terms of classify and prioritize the patients remotely.

Matched MeSH terms: Models, Theoretical
Optimized conditions for phytoremediation of diesel by Scirpus grossus in horizontal subsurface flow constructed wetlands (HSFCWs) using response surface methodology

Al-Baldawi IA, Sheikh Abdullah SR, Abu Hasan H, Suja F, Anuar N, Mushrifah I

J Environ Manage, 2014 Jul 1;140:152-9.
PMID: 24762527 DOI: 10.1016/j.jenvman.2014.03.007

This study investigated the optimum conditions for total petroleum hydrocarbon (TPH) removal from diesel-contaminated water using phytoremediation treatment with Scirpus grossus. In addition, TPH removal from sand was adopted as a second response. The optimum conditions for maximum TPH removal were determined through a Box-Behnken Design. Three operational variables, i.e. diesel concentration (0.1, 0.175, 0.25% Vdiesel/Vwater), aeration rate (0, 1 and 2 L/min) and retention time (14, 43 and 72 days), were investigated by setting TPH removal and diesel concentration as the maximum, retention time within the given range, and aeration rate as the minimum. The optimum conditions were found to be a diesel concentration of 0.25% (Vdiesel/Vwater), a retention time of 63 days and no aeration with an estimated maximum TPH removal from water and sand of 76.3 and 56.5%, respectively. From a validation test of the optimum conditions, it was found that the maximum TPH removal from contaminated water and sand was 72.5 and 59%, respectively, which was a 5 and 4.4% deviation from the values given by the Box-Behnken Design, providing evidence that S. grossus is a Malaysian native plant that can be used to remediate wastewater containing hydrocarbons.

Matched MeSH terms: Models, Theoretical
Fulltext Prediction of conductivity by adaptive neuro-fuzzy model

Akbarzadeh S, Arof AK, Ramesh S, Khanmirzaei MH, Nor RM

PLoS One, 2014;9(3):e92241.
PMID: 24658582 DOI: 10.1371/journal.pone.0092241

Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity.

Matched MeSH terms: Models, Theoretical
Fulltext Development of biological movement recognition by interaction between active basis model and fuzzy optical flow division

Yousefi B, Loo CK

ScientificWorldJournal, 2014;2014:238234.
PMID: 24883361 DOI: 10.1155/2014/238234

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach.

Matched MeSH terms: Models, Theoretical
Fulltext Dual-band operation of a microstrip patch antenna on a Duroid 5870 substrate for Ku- and K-bands

Islam MM, Islam MT, Faruque MR

ScientificWorldJournal, 2013;2013:378420.
PMID: 24385878 DOI: 10.1155/2013/378420

The dual-band operation of a microstrip patch antenna on a Duroid 5870 substrate for Ku- and K-bands is presented. The fabrication of the proposed antenna is performed with slots and a Duroid 5870 dielectric substrate and is excited by a 50 Ω microstrip transmission line. A high-frequency structural simulator (HFSS) is used which is based on the finite element method (FEM) in this research. The measured impedance bandwidth (2 : 1 VSWR) achieved is 1.07 GHz (15.93 GHz-14.86 GHz) on the lower band and 0.94 GHz (20.67-19.73 GHz) on the upper band. A stable omnidirectional radiation pattern is observed in the operating frequency band. The proposed prototype antenna behavior is discussed in terms of the comparisons of the measured and simulated results.

Matched MeSH terms: Models, Theoretical
Fulltext MATIN: a random network coding based framework for high quality peer-to-peer live video streaming

Barekatain B, Khezrimotlagh D, Aizaini Maarof M, Ghaeini HR, Salleh S, Quintana AA, et al.

PLoS One, 2013;8(8):e69844.
PMID: 23940530 DOI: 10.1371/journal.pone.0069844

In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay.

Matched MeSH terms: Models, Theoretical
Fulltext Preparation and characterization of poly(ethyl hydrazide)-grafted oil palm empty fruit bunch fibre for the removal of Cu(II) ions from an aqueous environment

Johari IS, Yusof NA, Haron MJ, Nor SM

Molecules, 2013 Jul 18;18(7):8461-72.
PMID: 23873385 DOI: 10.3390/molecules18078461

Poly(ethyl hydrazide)-grafted oil palm empty fruit bunch fibre (peh-g-opefb) was successfully prepared by heating poly(methyl acrylate)-grafted opefb (pma-g-opefb) at 60 °C for 4 h with a solution of hydrazine hydrate (15% v/v) in ethanol. The Fourier transform infrared spectrum of the product shows a secondary amine peak at 3267 cm⁻¹, with amide carbonyl peaks at 1729 cm⁻¹ and 1643 cm⁻¹. The chelating ability of peh-g-opefb was tested with copper ion in aqueous solution. A batch adsorption study revealed that maximum adsorption of copper ion was achieved at pH 5. An isotherm study showed the adsorption follows a Langmuir model, with a maximum adsorption capacity of 43.48 mg g-1 at 25 °C. A kinetic study showed that the adsorption of copper ion rapidly reaches equilibrium and follows a pseudo-second-order kinetic model, with a constant rate of 7.02 × 10⁻⁴ g mg⁻¹ min⁻¹ at 25 °C. The Gibbs free energy, ∆G⁰, value is negative, indicating a spontaneous sorption process. Entropy, ∆S⁰, gives a positive value, indicating that the system is becoming increasingly disordered after the adsorption of copper ion. A positive enthalpy value, ∆H⁰, shows that the endothermic process takes place during the adsorption and is more favourable at high temperatures.

Matched MeSH terms: Models, Theoretical
Fulltext Feature selection methods for optimizing clinicopathologic input variables in oral cancer prognosis

Chang SW, Kareem SA, Kallarakkal TG, Merican AF, Abraham MT, Zain RB

Asian Pac J Cancer Prev, 2011;12(10):2659-64.
PMID: 22320970

The incidence of oral cancer is high for those of Indian ethnic origin in Malaysia. Various clinical and pathological data are usually used in oral cancer prognosis. However, due to time, cost and tissue limitations, the number of prognosis variables need to be reduced. In this research, we demonstrated the use of feature selection methods to select a subset of variables that is highly predictive of oral cancer prognosis. The objective is to reduce the number of input variables, thus to identify the key clinicopathologic (input) variables of oral cancer prognosis based on the data collected in the Malaysian scenario. Two feature selection methods, genetic algorithm (wrapper approach) and Pearson's correlation coefficient (filter approach) were implemented and compared with single-input models and a full-input model. The results showed that the reduced models with feature selection method are able to produce more accurate prognosis results than the full-input model and single-input model, with the Pearson's correlation coefficient achieving the most promising results.

Matched MeSH terms: Models, Theoretical
Isotherm equilibria of Mn²⁺ biosorption in drinking water treatment by locally isolated Bacillus species and sewage activated sludge

Hasan HA, Abdullah SR, Kofli NT, Kamarudin SK

J Environ Manage, 2012 Nov 30;111:34-43.
PMID: 22813857 DOI: 10.1016/j.jenvman.2012.06.027

Manganese (Mn(2+)) is one of the inorganic contaminant that causes problem to water treatment and water distribution due to the accumulation on water piping systems. In this study, Bacillus sp. and sewage activated sludge (SAS) were investigated as biosorbents in laboratory-scale experiments. The study showed that Bacillus sp. was a more effective biosorbent than SAS. The experimental data were fitted to the Langmuir (Langmuir-1 & Langmuir-2), Freundlich, Temkin, Dubinin-Radushkevich (D-R) and Redlich-Peterson (R-P) isotherms to obtain the characteristic parameters of each model. Mn(2+) biosorption by Bacillus sp. was found to be significantly better fitted to the Langmuir-1 isotherm than the other isotherms, while the D-R isotherm was the best fit for SAS; i.e., the χ(2) value was smaller than that for the Freundlich, Temkin, and R-P isotherms. According to the evaluation using the Langmuir-1 isotherm, the maximum biosorption capacities of Mn(2+) onto Bacillus sp. and SAS were 43.5 mg Mn(2+)/g biomass and 12.7 mg Mn(2+)/g biomass, respectively. The data fitted using the D-R isotherm showed that the Mn(2+) biosorption processes by both Bacillus sp. and SAS occurred via the chemical ion-exchange mechanism between the functional groups and Mn(2+) ion.

Matched MeSH terms: Models, Theoretical
Flow and sediment yield simulations for Bukit Merah Reservoir catchment, Malaysia: a case study

Hasan ZA, Hamidon N, Yusof MS, Ghani AA

Water Sci Technol, 2012;66(10):2170-6.
PMID: 22949248 DOI: 10.2166/wst.2012.432

Bukit Merah Reservoir is the main potable and irrigation water source for Kerian District, Perak State, Malaysia. For the past two decades, the reservoir has experienced water stress. Land-use activities have been identified as the contributor of the sedimentation. The Soil and Water Assessment Tool (SWAT) was used to simulate and quantify the impacts of land-use change in the reservoir watershed. The SWAT was calibrated and two scenarios were constructed representing projected land use in the year 2015 and hypothetical land use to represent extensive land-use change in the catchment area. The simulation results based on 17 years of rainfall records indicate that average water quantity will not be significantly affected but the ground water storage will decrease and suspended sediment will increase. Ground water decrease and sediment yield increase will exacerbate the Bukit Merah Reservoir operation problem.

Matched MeSH terms: Models, Theoretical
Regional landfills methane emission inventory in Malaysia

Abushammala MF, Noor Ezlin Ahmad Basri, Basri H, Ahmed Hussein El-Shafie, Kadhum AA

Waste Manag Res, 2011 Aug;29(8):863-73.
PMID: 20858637 DOI: 10.1177/0734242X10382064

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.

Matched MeSH terms: Models, Theoretical
Kinetic modeling and half life study on bioremediation of crude oil dispersed by Corexit 9500

Zahed MA, Aziz HA, Isa MH, Mohajeri L, Mohajeri S, Kutty SR

J Hazard Mater, 2011 Jan 30;185(2-3):1027-31.
PMID: 21041026 DOI: 10.1016/j.jhazmat.2010.10.009

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.

Matched MeSH terms: Models, Theoretical
Unsteady magnetohydrodynamic blood flow through irregular multi-stenosed arteries

Mustapha N, Amin N, Chakravarty S, Mandal PK

Comput Biol Med, 2009 Oct;39(10):896-906.
PMID: 19665698 DOI: 10.1016/j.compbiomed.2009.07.004

Flow of an electrically conducting fluid characterizing blood through the arteries having irregular shaped multi-stenoses in the environment of a uniform transverse magnetic-field is analysed. The flow is considered to be axisymmetric with an outline of the irregular stenoses obtained from a three-dimensional casting of a mild stenosed artery, so that the physical problem becomes more realistic from the physiological point of view. The marker and cell (MAC) and successive-over-relaxation (SOR) methods are respectively used to solve the governing unsteady magnetohydrodynamic (MHD) equations and pressure-Poisson equation quantitatively and to observe the flow separation. The results obtained show that the flow separates mostly towards the downstream of the multi-stenoses. However, the flow separation region keeps on shrinking with the increasing intensity of the magnetic-field which completely disappears with sufficiently large value of the Hartmann number. The present observations certainly have some clinical implications relating to magnetotherapy which help reducing the complex flow separation zones causing flow disorder leading to the formation and progression of the arterial diseases.

Matched MeSH terms: Models, Theoretical

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