Displaying publications 1 - 20 of 252 in total

Abstract:
Sort:
  1. Municipal solid waste management in Malaysia: practices and challenges
    Manaf LA, Samah MA, Zukki NI
    Waste Manag, 2009 Nov;29(11):2902-6.
    PMID: 19540745 DOI: 10.1016/j.wasman.2008.07.015
    Rapid economic development and population growth, inadequate infrastructure and expertise, and land scarcity make the management of municipal solid waste become one of Malaysia's most critical environmental issues. The study is aimed at evaluating the generation, characteristics, and management of solid waste in Malaysia based on published information. In general, the per capita generation rate is about 0.5-0.8 kg/person/day in which domestic waste is the primary source. Currently, solid waste is managed by the Ministry of Housing and Local Government, with the participation of the private sector. A new institutional and legislation framework has been structured with the objectives to establish a holistic, integrated, and cost-effective solid waste management system, with an emphasis on environmental protection and public health. Therefore, the hierarchy of solid waste management has given the highest priority to source reduction through 3R, intermediate treatment and final disposal.
    Matched MeSH terms: Forecasting
  2. The future of medical education in Malaysia
    Zaini A
    Med J Malaysia, 2002 Dec;57 Suppl E:5-7.
    PMID: 12733184
    Matched MeSH terms: Forecasting
  3. Comparing the accuracy of density forecasts from competing GARCH models
    Abu Hassan Shaari Mohd Nor, Ahmad Shamiri, Zaidi Isa
    Sains Malaysiana, 2009;38.
    In this research we introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. Use of KLIC is practically attractive as well as convenient, given its equivalent of the widely used LR test. We include an illustrative simulation to compare a set of distributions, including symmetric and asymmetric distribution, and a family of GARCH volatility models. Our results on simulated data show that the choice of the conditional distribution appears to be a more dominant factor in determining the adequacy and accuracy (quality) of density forecasts than the choice of volatility model.
    Matched MeSH terms: Forecasting
  4. The birth prevalence, severity, and temporal trends of congenital heart disease in the middle-income country: A population-based study
    Mat Bah MN, Sapian MH, Jamil MT, Abdullah N, Alias EY, Zahari N
    Congenit Heart Dis, 2018 Nov;13(6):1012-1027.
    PMID: 30289622 DOI: 10.1111/chd.12672
    OBJECTIVES: There is limited data on congenital heart disease (CHD) from the lower- and middle-income country. We aim to study the epidemiology of CHD with the specific objective to estimate the birth prevalence, severity, and its trend over time.

    DESIGN: A population-based study with data retrieved from the Pediatric Cardiology Clinical Information System, a clinical registry of acquired and congenital heart disease for children.

    SETTING: State of Johor, Malaysia.

    PATIENTS: All children (0-12 years of age) born in the state of Johor between January 2006 and December 2015.

    INTERVENTION: None.

    OUTCOME MEASURE: The birth prevalence, severity, and temporal trend over time.

    RESULTS: There were 531,904 live births during the study period with 3557 new cases of CHD detected. Therefore, the birth prevalence of CHD was 6.7 per 1000 live births (LB) (95% confidence interval [CI]: 6.5-6.9). Of these, 38% were severe, 15% moderate, and 47% mild lesions. Hence, the birth prevalence of mild, moderate, and severe CHD was 3.2 (95% CI: 3.0-3.3), 0.9 (95% CI: 0.9- 1.1), and 2.6 (95% CI: 2.4-2.7) per 1000 LB, respectively. There was a significant increase in the birth prevalence of CHD, from 5.1/1000 LB in 2006 to 7.8/1000 LB in 2015 (P 
    Matched MeSH terms: Forecasting*
  5. Telemedicine in the Malaysian Multimedia Super Corridor: towards personalized lifetime health plans
    Abidi SS, Yusoff Z
    Stud Health Technol Inform, 1999;68:283-8.
    PMID: 10724889
    The Malaysian Telemedicine initiative advocates a paradigm shift in healthcare delivery patterns by way of implementing a person-centred and wellness-focused healthcare system. This paper introduces the Malaysian Telemedicine vision, its functionality and associated operational conditions. In particular, we focus on the conceptualisation of one key Telemedicine component i.e. the Lifetime Health Plan (LHP) system--a distributed multimodule application for the periodic monitoring and generation of health-care advisories for all Malaysians. In line with the LHP project, we present an innovative healthcare delivery info-structure--LifePlan--that aims to provide life-long, pro-active, personalised, wellness-oriented healthcare services to assist individuals to manage and interpret their health needs. Functionally, LifePlan based healthcare services are delivered over the WWW, packaged as Personalised Lifetime Health Plans that allow individuals to both monitor their health status and to guide them in healthcare planning.
    Matched MeSH terms: Forecasting
  6. Virotherapy: Current Trends and Future Prospects for Treatment of Colon and Rectal Malignancies
    Lee CL, Veeramani S, Molouki A, Lim SHE, Thomas W, Chia SL, et al.
    Cancer Invest, 2019;37(8):393-414.
    PMID: 31502477 DOI: 10.1080/07357907.2019.1660887
    Colorectal cancer (CRC) is one of the most common malignancies. In recent decades, early diagnosis and conventional therapies have resulted in a significant reduction in mortality. However, late stage metastatic disease still has very limited effective treatment options. There is a growing interest in using viruses to help target therapies to tumour sites. In recent years the evolution of immunotherapy has emphasised the importance of directing the immune system to eliminate tumour cells; we aim to give a state-of-the-art over-view of the diverse viruses that have been investigated as potential oncolytic agents for the treatment of CRC.
    Matched MeSH terms: Forecasting
  7. Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model
    Younes MK, Nopiah ZM, Basri NE, Basri H, Abushammala MF, Younes MY
    Waste Manag, 2016 Sep;55:3-11.
    PMID: 26522806 DOI: 10.1016/j.wasman.2015.10.020
    Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%.
    Matched MeSH terms: Forecasting
  8. Load forecasting using combination model of multiple linear regression with neural network for Malaysian city
    Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono Suhartono, Abdul Ghapor Hussin, Yong Zulina Zubairi
    Sains Malaysiana, 2018;47:419-426.
    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.
    Matched MeSH terms: Forecasting
  9. Multimedia environmental fate and transport model of dichlorodiphenyltrichloroethane (DDT): case study sayong river watershed, Johor, Malaysia
    Ghani Z, Anuar A, Majid Z, Yoneda M
    Sains Malaysiana, 2017;46:2383-2392.
    This study describes the development of a multimedia environmental fate and transport model of dichlorodiphenyltrichloroethane (DDT) at Sungai Sayong watershed. Based on the latest estimated DDT emission, the DDT concentrations in air, soil, water and sediment as well as the transfer processes were simulated under the equilibrium and steady-state assumption. Model predictions suggested that soil and sediment was the dominant sink of DDT. The results showed that the model predicted was generally good agreement with field data. Compared with degradation reaction, advection outflow was more important processes occurred in the model. Sensitivities of the model estimates to input parameters were tested. The result showed that vapour pressure (Ps) and organic carbon water partition coefficient (KOC) were the most influential parameters for the model output. The model output-concentrations of DDT in multimedia environment is very important as it can be used in future for human exposure and risk assessment of organochlorine pesticides (OCPs) at Sungai Sayong Basin.
    Matched MeSH terms: Forecasting
  10. Fulltext Medical manpower needs by the year 2000
    Yeoh PH
    Med J Malaysia, 1988 Sep;43(3):195-9.
    PMID: 3241576
    Matched MeSH terms: Forecasting
  11. Fulltext PASS-predicted hepatoprotective activity of Caesalpinia sappan in thioacetamide-induced liver fibrosis in rats
    Kadir FA, Kassim NM, Abdulla MA, Kamalidehghan B, Ahmadipour F, Yehye WA
    ScientificWorldJournal, 2014;2014:301879.
    PMID: 24701154 DOI: 10.1155/2014/301879
    The antifibrotic effects of traditional medicinal herb Caesalpinia sappan (CS) extract on liver fibrosis induced by thioacetamide (TAA) and the expression of transforming growth factor β1 (TGF-β1), α-smooth muscle actin (αSMA), and proliferating cell nuclear antigen (PCNA) in rats were studied. A computer-aided prediction of antioxidant and hepatoprotective activities was primarily performed with the Prediction Activity Spectra of the Substance (PASS) Program. Liver fibrosis was induced in male Sprague Dawley rats by TAA administration (0.03% w/v) in drinking water for a period of 12 weeks. Rats were divided into seven groups: control, TAA, Silymarin (SY), and CS 300 mg/kg body weight and 100 mg/kg groups. The effect of CS on liver fibrogenesis was determined by Masson's trichrome staining, immunohistochemical analysis, and western blotting. In vivo determination of hepatic antioxidant activities, cytochrome P450 2E1 (CYP2E1), and matrix metalloproteinases (MPPS) was employed. CS treatment had significantly increased hepatic antioxidant enzymes activity in the TAA-treated rats. Liver fibrosis was greatly alleviated in rats when treated with CS extract. CS treatment was noted to normalize the expression of TGF-β1, αSMA, PCNA, MMPs, and TIMP1 proteins. PASS-predicted plant activity could efficiently guide in selecting a promising pharmaceutical lead with high accuracy and required antioxidant and hepatoprotective properties.
    Matched MeSH terms: Forecasting
  12. Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
    Fu M, Le C, Fan T, Prakapovich R, Manko D, Dmytrenko O, et al.
    Environ Sci Pollut Res Int, 2021 Dec;28(45):64818-64829.
    PMID: 34318419 DOI: 10.1007/s11356-021-15574-y
    The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.
    Matched MeSH terms: Forecasting
  13. Fulltext Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America
    Hameed MM, Razali SFM, Mohtar WHMW, Rahman NA, Yaseen ZM
    PLoS One, 2023;18(10):e0290891.
    PMID: 37906556 DOI: 10.1371/journal.pone.0290891
    The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.
    Matched MeSH terms: Forecasting
  14. Is it worth repairing rheumatic mitral valve disease in children? Long-term outcomes of an aggressive approach to rheumatic mitral valve repair compared to replacement in young patients
    Krishna Moorthy PS, Sivalingam S, Dillon J, Kong PK, Yakub MA
    Interact Cardiovasc Thorac Surg, 2019 02 01;28(2):191-198.
    PMID: 30085022 DOI: 10.1093/icvts/ivy234
    OBJECTIVES: Contemporary experience in mitral valve (MV) repair for children with rheumatic heart disease (RHD) is limited, despite the potential advantages of repair over replacement. We reviewed our long-term outcomes of rheumatic MV repair and compared them with the outcomes of MV replacement in children with RHD.

    METHODS: This study is a review of 419 children (≤18 years) with RHD who underwent primary isolated MV surgery between 1992 and 2015, which comprised MV repair (336 patients; 80.2%) and MV replacement (83 patients; 19.8%). The replacement group included mechanical MV replacements (MMVRs) (n = 69 patients; 16.5%) and bioprosthetic MV replacements (n = 14 patients; 3.3%). The mean age with standard deviation at the time of operation was 12.5 ± 3.5 (2-18) years. Mitral regurgitation (MR) was predominant in 390 (93.1%) patients, and 341 (81.4%) patients showed ≥3+ MR. The modified Carpentier reconstructive techniques were used for MV repair.

    RESULTS: Overall early mortality was 1.7% (7 patients). The mean follow-up was 5.6 years (range 0-22.3 years; 94.7% complete). Survival of patients who underwent repair was 93.9% both at 10 and 20 years, which was superior than that of replacement (P 

    Matched MeSH terms: Forecasting*
  15. Fulltext Common institutional investors and the quality of management earnings forecasts-Empirical and machine learning evidences
    Yang S, Li X, Jiang Z, Xiao M
    PLoS One, 2023;18(10):e0290126.
    PMID: 37844110 DOI: 10.1371/journal.pone.0290126
    Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.
    Matched MeSH terms: Forecasting
  16. Nanospray Drying Technology: Existing Limitations and Future Challenges
    Wui WT
    Recent Pat Drug Deliv Formul, 2015;9(3):185-6.
    PMID: 25966873
    Matched MeSH terms: Forecasting
  17. An interactively recurrent functional neural fuzzy network with fuzzy differential evolution and its applications
    Lin CJ, Lin HY, Yu CY, Wu CF
    Sains Malaysiana, 2015;44:1721-1728.
    In this paper, an interactively recurrent functional neural fuzzy network (IRFNFN) with fuzzy differential evolution (FDE)
    learning method was proposed for solving the control and the prediction problems. The traditional differential evolution
    (DE) method easily gets trapped in a local optimum during the learning process, but the proposed fuzzy differential
    evolution algorithm can overcome this shortcoming. Through the information sharing of nodes in the interactive layer,
    the proposed IRFNFN can effectively reduce the number of required rule nodes and improve the overall performance of
    the network. Finally, the IRFNFN model and associated FDE learning algorithm were applied to the control system of the
    water bath temperature and the forecast of the sunspot number. The experimental results demonstrate the effectiveness
    of the proposed method.
    Matched MeSH terms: Forecasting
  18. Fulltext The future of the GP
    Wilkinson IE
    Br J Gen Pract, 1992 Feb;42(355):84.
    PMID: 1493024
    Matched MeSH terms: Forecasting
  19. Fulltext Financing the future of WHO
    Gostin LO, Klock KA, Clark H, Diop FZ, Jayasuriya D, Mahmood J, et al.
    Lancet, 2022 Apr 16;399(10334):1445-1447.
    PMID: 35338858 DOI: 10.1016/S0140-6736(22)00533-5
    Matched MeSH terms: Forecasting
  20. Forfeiture of the profits and proceeds derived from drug trafficking: thoughts on future action in Malaysia
    Vohrah KC
    Bull Narc, 1984 Oct-Dec;36(4):31-41.
    PMID: 6570698
    While the Dangerous Drugs Act 1952 of Malaysia has been amended to take into account changing patterns of drug abuse and trafficking, it lacks provisions for the mandatory forfeiture of proceeds derived from drug trafficking. Nor do the general powers of forfeiture in the Criminal Procedure Code of the country extend to such proceeds. To meet further changing patterns of drug trafficking involving criminal syndicate leaders, who rarely incriminate themselves through overt and detectable acts, Malaysia has a bill in Parliament the purpose of which, when it becomes law, is to detain without trial, upon cogent evidence, persons who have been associated with any activity relating to or involving drug trafficking, and to prevent them from further committing drug crimes. In addition, serious thinking has been given to the possibility of adopting, within the constraints of the Malaysian Constitution, a law on forfeiture of the proceeds derived from drug trafficking. There are, in this respect, several problems to be resolved, such as the secrecy of bank accounts and taxpayers' returns, which might make it difficult to trace proceeds and to keep track of tainted money being remitted abroad, although it is believed that such problems could be overcome by domestic measures. A more serious problem is the lack of international co-operation for investigations to be carried out outside national borders to trace, seize, freeze and secure the forfeiture of the proceeds of drug crimes located abroad.
    Matched MeSH terms: Forecasting
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links