Displaying publications 1 - 20 of 311 in total

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  1. Zero-inflated Conway-Maxwell Poisson Distribution to Analyze Discrete Data
    Sim SZ, Gupta RC, Ong SH
    Int J Biostat, 2018 Jan 09;14(1).
    PMID: 29306919 DOI: 10.1515/ijb-2016-0070
    In this paper, we study the zero-inflated Conway-Maxwell Poisson (ZICMP) distribution and develop a regression model. Score and likelihood ratio tests are also implemented for testing the inflation/deflation parameter. Simulation studies are carried out to examine the performance of these tests. A data example is presented to illustrate the concepts. In this example, the proposed model is compared to the well-known zero-inflated Poisson (ZIP) and the zero- inflated generalized Poisson (ZIGP) regression models. It is shown that the fit by ZICMP is comparable or better than these models.
    Matched MeSH terms: Models, Statistical*
  2. Wavelet neural networks applied to pulping of oil palm fronds
    Zainuddin Z, Wan Daud WR, Pauline O, Shafie A
    Bioresour Technol, 2011 Dec;102(23):10978-86.
    PMID: 21996481 DOI: 10.1016/j.biortech.2011.09.080
    In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.
    Matched MeSH terms: Models, Statistical
  3. Water treatment and artificial intelligence techniques: a systematic literature review research
    Ismail W, Niknejad N, Bahari M, Hendradi R, Zaizi NJM, Zulkifli MZ
    Environ Sci Pollut Res Int, 2023 Jun;30(28):71794-71812.
    PMID: 34609681 DOI: 10.1007/s11356-021-16471-0
    As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.
    Matched MeSH terms: Models, Statistical
  4. Fulltext Validating the Physical Activity and Leisure Motivation Scale (PALMS)
    Molanorouzi K, Khoo S, Morris T
    BMC Public Health, 2014;14:909.
    PMID: 25182130 DOI: 10.1186/1471-2458-14-909
    Although there is abundant evidence to recommend a physically active lifestyle, adult physical activity (PA) levels have declined over the past two decades. In order to understand why this happens, numerous studies have been conducted to uncover the reasons for people's participation in PA. Often, the measures used were not broad enough to reflect all the reasons for participation in PA. The Physical Activity and Leisure Motivation Scale (PALMS) was created to be a comprehensive tool measuring motives for participating in PA. This 40-item scale related to participation in sport and PA is designed for adolescents and adults. Five items constitute each of the eight sub-scales (mastery, enjoyment, psychological condition, physical condition, appearance, other's expectations, affiliation, competition/ego) reflecting motives for participation in PA that can be categorized as features of intrinsic and extrinsic motivation based on self-determination theory. The aim of the current study was to validate the PALMS in the cultural context of Malaysia, including to assess how well the PALMS captures the same information as the Recreational Exercise Motivation Measure (REMM).
    Matched MeSH terms: Models, Statistical
  5. Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction
    Roslan R, Othman RM, Shah ZA, Kasim S, Asmuni H, Taliba J, et al.
    Comput Biol Med, 2010 Jun;40(6):555-64.
    PMID: 20417930 DOI: 10.1016/j.compbiomed.2010.03.009
    Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
    Matched MeSH terms: Models, Statistical*
  6. Fulltext Utilization of a Mixture Cure Rate Model based on the Generalized Modified Weibull Distribution for the Analysis of Leukemia Patients
    Omer ME, Abu Bakar M, Adam M, Mustafa M
    Asian Pac J Cancer Prev, 2021 Apr 01;22(4):1045-1053.
    PMID: 33906295 DOI: 10.31557/APJCP.2021.22.4.1045
    OBJECTIVE: Cure rate models are survival models, commonly applied to model survival data with a cured fraction. In the existence of a cure rate, if the distribution of survival times for susceptible patients is specified, researchers usually prefer cure models to parametric models. Different distributions can be assumed for the survival times, for instance, generalized modified Weibull (GMW), exponentiated Weibull (EW), and log-beta Weibull. The purpose of this study is to select the best distribution for uncured patients' survival times by comparing the mixture cure models based on the GMW distribution and its particular cases.

    MATERIALS AND METHODS: A data set of 91 patients with high-risk acute lymphoblastic leukemia (ALL) followed for five years from 1982 to 1987 was chosen for fitting the mixture cure model. We used the maximum likelihood estimation technique via R software 3.6.2 to obtain the estimates for parameters of the proposed model in the existence of cure rate, censored data, and covariates. For the best model choice, the Akaike information criterion (AIC) was implemented.

    RESULTS: After comparing different parametric models fitted to the data, including or excluding cure fraction, without covariates, the smallest AIC values were obtained by the EW and the GMW distributions, (953.31/969.35) and (955.84/975.99), respectively. Besides, assuming a mixture cure model based on GMW with covariates, an estimated ratio between cure fractions for allogeneic and autologous bone marrow transplant groups (and its 95% confidence intervals) were 1.42972 (95% CI: 1.18614 - 1.72955).

    CONCLUSION: The results of this study reveal that the EW and the GMW distributions are the best choices for the survival times of Leukemia patients.
    .

    Matched MeSH terms: Models, Statistical*
  7. Using autoencoders for mammogram compression
    Tan CC, Eswaran C
    J Med Syst, 2011 Feb;35(1):49-58.
    PMID: 20703586 DOI: 10.1007/s10916-009-9340-3
    This paper presents the results obtained for medical image compression using autoencoder neural networks. Since mammograms (medical images) are usually of big sizes, training of autoencoders becomes extremely tedious and difficult if the whole image is used for training. We show in this paper that the autoencoders can be trained successfully by using image patches instead of the whole image. The compression performances of different types of autoencoders are compared based on two parameters, namely mean square error and structural similarity index. It is found from the experimental results that the autoencoder which does not use Restricted Boltzmann Machine pre-training yields better results than those which use this pre-training method.
    Matched MeSH terms: Models, Statistical
  8. Fulltext Transverse cerebellar diameter as a sonographic parameter for estimating gestational age: an experience from Yemeni population
    Muneera A. S. Yahya, Husni A. Al- Goshae, Hameed M. Aklan, Maha Abdul-aziz, Abdullah A. Al-Mikhlafy
    Malaysian Journal of Medicine and Health Sciences, 2020;16(107):23-27.
    MyJurnal
    Introduction: Estimation of gestational age (GA) is clinically crucial for managing pregnancy and assessing the foetal anatomy, growth and development. Transverse cerebellar diameter (TCD) has been reported as an accurate tool for dating the pregnancy. This study aimed to determine the accuracy of foetal TCD for dating the pregnancy and to con- struct a reference chart for GA of Yemeni foetuses. Methods: We conducted this prospective cross-sectional study among 400 Yemeni pregnant women between 18 and 40 weeks of gestation provided that they were with known last menstrual period and singleton normal pregnancies. Sonographic TCDs were measured for each foetus. The mean TCD was measured for gestational weeks separately, and a polynomial regression model was then used to predict the GA by TCD. Results: There was a robust correlation between GA and TCD (r = 0.995, p
    Matched MeSH terms: Models, Statistical
  9. Thresholds for decision-making: informing the cost-effectiveness and affordability of rotavirus vaccines in Malaysia
    Loganathan T, Ng CW, Lee WS, Hutubessy RCW, Verguet S, Jit M
    Health Policy Plan, 2018 Mar 01;33(2):204-214.
    PMID: 29228339 DOI: 10.1093/heapol/czx166
    Cost-effectiveness thresholds (CETs) based on the Commission on Macroeconomics and Health (CMH) are extensively used in low- and middle-income countries (LMICs) lacking locally defined CETs. These thresholds were originally intended for global and regional prioritization, and do not reflect local context or affordability at the national level, so their value for informing resource allocation decisions has been questioned. Using these thresholds, rotavirus vaccines are widely regarded as cost-effective interventions in LMICs. However, high vaccine prices remain a barrier towards vaccine introduction. This study aims to evaluate the cost-effectiveness, affordability and threshold price of universal rotavirus vaccination at various CETs in Malaysia. Cost-effectiveness of Rotarix and RotaTeq were evaluated using a multi-cohort model. Pan American Health Organization Revolving Fund's vaccine prices were used as tender price, while the recommended retail price for Malaysia was used as market price. We estimate threshold prices defined as prices at which vaccination becomes cost-effective, at various CETs reflecting economic theories of human capital, societal willingness-to-pay and marginal productivity. A budget impact analysis compared programmatic costs with the healthcare budget. At tender prices, both vaccines were cost-saving. At market prices, cost-effectiveness differed with thresholds used. At market price, using 'CMH thresholds', Rotarix programmes were cost-effective and RotaTeq were not cost-effective from the healthcare provider's perspective, while both vaccines were cost-effective from the societal perspective. Using other CETs, both vaccines were not cost-effective at market price, from the healthcare provider's and societal perspectives. At tender and cost-effective prices, rotavirus vaccination cost ∼1 and 3% of the public health budget, respectively. Using locally defined thresholds, rotavirus vaccination is cost-effective at vaccine prices in line with international tenders, but not at market prices. Thresholds representing marginal productivity are likely to be lower than those reflecting human capital and individual preference measures, and may be useful in determining affordable vaccine prices.
    Matched MeSH terms: Models, Statistical
  10. The use of response surface methodology for enhanced production of a thermostable bacterial lipase in a novel yeast system
    Abu ML, Mohammad R, Oslan SN, Salleh AB
    Prep Biochem Biotechnol, 2021;51(4):350-360.
    PMID: 32940138 DOI: 10.1080/10826068.2020.1818256
    A thermostable bacterial lipase from Geobacillus zalihae was expressed in a novel yeast Pichia sp. strain SO. The preliminary expression was too low and discourages industrial production. This study sought to investigate the optimum conditions for T1 lipase production in Pichia sp. strain SO. Seven medium conditions were investigated and optimized using Response Surface Methodology (RSM). Five responding conditions namely; temperature, inoculum size, incubation time, culture volume and agitation speed observed through Plackett-Burman Design (PBD) method had a significant effect on T1 lipase production. The medium conditions were optimized using Box-Behnken Design (BBD). Investigations reveal that the optimum conditions for T1 lipase production and Biomass concentration (OD600) were; Temperature 31.76 °C, incubation time 39.33 h, culture volume 132.19 mL, inoculum size 3.64%, and agitation speed of 288.2 rpm with a 95% PI low as; 12.41 U/mL and 95% PI high of 13.65 U/mL with an OD600 of; 95% PI low as; 19.62 and 95% PI high as; 22.62 as generated by the software was also validated. These predicted parameters were investigated experimentally and the experimental result for lipase activity observed was 13.72 U/mL with an OD600 of 24.5. At these optimum conditions, there was a 3-fold increase on T1 lipase activity. This study is the first to develop a statistical model for T1 lipase production and biomass concentration in Pichia sp. Strain SO. The optimized production of T1 lipase presents a choice for its industrial application.
    Matched MeSH terms: Models, Statistical*
  11. The use of BLRP model for disaggregating daily rainfall affected by monsoon in Peninsular Malaysia
    Zulkifli Yusop, Harisaweni, Fadhilah Yusof
    Sains Malaysiana, 2016;45:87-97.
    Rainfall intensity is the main input variable in various hydrological analysis and modeling. Unfortunately, the quality of rainfall data is often poor and reliable data records are available at coarse intervals such as yearly, monthly and daily. Short interval rainfall records are scarce because of high cost and low reliability of the measurement and the monitoring systems. One way to solve this problem is by disaggregating the coarse intervals to generate the short one using the stochastic method. This paper describes the use of the Bartlett Lewis Rectangular Pulse (BLRP) model. The method was used to disaggregate 10 years of daily data for generating hourly data from 5 rainfall stations in Kelantan as representative area affected by monsoon period and 5 rainfall stations in Damansara affected by inter-monsoon period. The models were evaluated on their ability to reproduce standard and extreme rainfall model statistics derived from the historical record over disaggregation simulation results. The disaggregation of daily to hourly rainfall produced monthly and daily means and variances that closely match the historical records. However, for the disaggregation of daily to hourly rainfall, the standard deviation values are lower than the historical ones. Despite the marked differences in the standard deviation, both data series exhibit similar patterns and the model adequately preserve the trends of all the properties used in evaluating its performances.
    Matched MeSH terms: Models, Statistical
  12. The trajectory of COVID-19 scenario in Malaysia: facing the unprecedented
    Minhat HS, Kadir Shahar H
    Curr Med Res Opin, 2020 08;36(8):1309-1311.
    PMID: 32569488 DOI: 10.1080/03007995.2020.1786680
    Background: Like other affected countries around the globe, Malaysia is shocked by the Coronavirus disease 2019, which is also known as COVID-19.Aims: This commentary article discusses the COVID-19 scenario in Malaysia, particularly in relation to the sudden increase in the number of new cases related to an international mass gathering.Findings: Projection through modelling helps the relevant authorities to act quickly and effectively, including enforcement of physical and social distancing. Modelling also assists in understanding the link between the biological processes that underpin transmission events and the population-level dynamics of the disease.Conclusion: There is no one-size-fits-all approach in managing disease outbreak. The fight against COVID-19 very much depends on their attitude during the 14-day Movement Control Order (MCO) which has been extended recently.
    Matched MeSH terms: Models, Statistical
  13. The relationship between dental anxiety and dental decay experience in antenatal mothers
    Esa R, Savithri V, Humphris G, Freeman R
    Eur J Oral Sci, 2010 Feb;118(1):59-65.
    PMID: 20156266 DOI: 10.1111/j.1600-0722.2009.00701.x
    The aim of this study was to investigate the relationship between dental anxiety and dental decay experience among antenatal mothers attending Maternal and Child Health clinics in Malaysia. A cross-sectional study was conducted on a consecutive sample of 407 antenatal mothers in Seremban, Malaysia. The questionnaire consisted of participants' demographic profile and the Dental Fear Survey. The D(3cv)MFS was employed as the outcome measure and was assessed by a single examiner (intraclass correlation = 0.98). A structural equation model was designed to inspect the relationship between dental anxiety and dental decay experience. The mean Dental Fear Survey score for all participants was 35.1 [95% confidence interval (34.0, 36.3)]. The mean D(3cv)MFS score was 10.8 [95% confidence interval (9.5, 12.1)]. Participants from low socio-economic status groups had significantly higher D(3cv)MFS counts than those from high socio-economic status groups. The path model with dental anxiety and socio-economic status as predictors of D(3cv)MFS showed satisfactory fit. The correlation between dental anxiety and dental decay experience was 0.30 (standardized estimate), indicating a positive association. Socio-economic status was also statistically significantly associated with the D(3cv)MFS count (beta = 0.19). This study presented robust evidence for the significant relationship between dental anxiety and dental decay experience in antenatal mothers.
    Matched MeSH terms: Models, Statistical
  14. The present and future disease burden of hepatitis C virus infections with today's treatment paradigm: Volume 4
    Chan HLY, Chen CJ, Omede O, Al Qamish J, Al Naamani K, Bane A, et al.
    J Viral Hepat, 2017 10;24 Suppl 2:25-43.
    PMID: 29105283 DOI: 10.1111/jvh.12760
    Factors influencing the morbidity and mortality associated with viremic hepatitis C virus (HCV) infection change over time and place, making it difficult to compare reported estimates. Models were developed for 17 countries (Bahrain, Bulgaria, Cameroon, Colombia, Croatia, Dominican Republic, Ethiopia, Ghana, Hong Kong, Jordan, Kazakhstan, Malaysia, Morocco, Nigeria, Qatar and Taiwan) to quantify and characterize the viremic population as well as forecast the changes in the infected population and the corresponding disease burden from 2015 to 2030. Model inputs were agreed upon through expert consensus, and a standardized methodology was followed to allow for comparison across countries. The viremic prevalence is expected to remain constant or decline in all but four countries (Ethiopia, Ghana, Jordan and Oman); however, HCV-related morbidity and mortality will increase in all countries except Qatar and Taiwan. In Qatar, the high-treatment rate will contribute to a reduction in total cases and HCV-related morbidity by 2030. In the remaining countries, however, the current treatment paradigm will be insufficient to achieve large reductions in HCV-related morbidity and mortality.
    Matched MeSH terms: Models, Statistical*
  15. The performance of multiple imputations for different number of imputations
    Ser G, Keskin S, Can Yilmaz M
    Sains Malaysiana, 2016;45:1755-1761.
    Multiple imputation method is a widely used method in missing data analysis. The method consists of a three-stage
    process including imputation, analyzing and pooling. The number of imputations to be selected in the imputation step
    in the first stage is important. Hence, this study aimed to examine the performance of multiple imputation method at
    different numbers of imputations. Monotone missing data pattern was created in the study by deleting approximately 24%
    of the observations from the continuous result variable with complete data. At the first stage of the multiple imputation
    method, monotone regression imputation at different numbers of imputations (m=3, 5, 10 and 50) was performed. In the
    second stage, parameter estimations and their standard errors were obtained by applying general linear model to each
    of the complete data sets obtained. In the final stage, the obtained results were pooled and the effect of the numbers of
    imputations on parameter estimations and their standard errors were evaluated on the basis of these results. In conclusion,
    efficiency of parameter estimations at the number of imputation m=50 was determined as about 99%. Hence, at the
    determined missing observation rate, increase was determined in efficiency and performance of the multiple imputation
    method as the number of imputations increased.
    Matched MeSH terms: Models, Statistical
  16. Fulltext The performance of expectation maximization (EM) Algorithm in Gaussian Mixed Models (GMM)
    Mohd. Izhan Mohd. Yusoff, Mohd. Rizam Abu Bakar, Abu Hassan Shaari Mohd. Nor
    Pertanika Journal of Science & Technology, 2009;17(2):-.
    MyJurnal
    Expectation Maximization (EM) algorithm has experienced a significant increase in terms of usage in many fields of study. In this paper, the performance of the said algorithm in finding the Maximum Likelihood for the Gaussian Mixed Models (GMM), a probabilistic model normally used in fraud detection and recognizing a person’s voice in speech recognition field, is shown and discussed. At the end of the paper, some suggestions for future research works will also be given.
    Matched MeSH terms: Models, Statistical
  17. Fulltext The influence of lightning induced voltage on the distribution power line polymer insulators
    Izadi M, Abd Rahman MS, Ab-Kadir MZ, Gomes C, Jasni J, Hajikhani M
    PLoS One, 2017;12(2):e0172118.
    PMID: 28234930 DOI: 10.1371/journal.pone.0172118
    Protection of medium voltage (MV) overhead lines against the indirect effects of lightning is an important issue in Malaysia and other tropical countries. Protection of these lines against the indirect effects of lightning is a major concern and can be improved by several ways. The choice of insulator to be used for instance, between the glass, ceramic or polymer, can help to improve the line performance from the perspective of increasing the breakdown strength. In this paper, the electrical performance of a 10 kV polymer insulator under different conditions for impulse, weather and insulator angle with respect to a cross-arm were studied (both experimental and modelling) and the results were discussed accordingly. Results show that the weather and insulator angle (with respect to the cross-arm) are surprisingly influenced the values of breakdown voltage and leakage current for both negative and positive impulses. Therefore, in order to select a proper protection system for MV lines against lightning induced voltage, consideration of the local information concerning the weather and also the insulator angles with respect to the cross-arm are very useful for line stability and performance.
    Matched MeSH terms: Models, Statistical
  18. The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach
    Taha Z, Musa RM, P P Abdul Majeed A, Alim MM, Abdullah MR
    Hum Mov Sci, 2018 Feb;57:184-193.
    PMID: 29248809 DOI: 10.1016/j.humov.2017.12.008
    Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme.
    Matched MeSH terms: Models, Statistical
  19. The effects of drainage basin geomorphometry on minimum low flow discharge: the study of small watershed in Kelang River Valley in Peninsular Malaysia
    Yunus AJ, Nakagoshi N, Salleh KO
    J Environ Sci (China), 2003 Mar;15(2):249-62.
    PMID: 12765268
    This study investigate the relationships between geomorphometric properties and the minimum low flow discharge of undisturbed drainage basins in the Taman Bukit Cahaya Seri Alam Forest Reserve, Peninsular Malaysia. The drainage basins selected were third-order basins so as to facilitate a common base for sampling and performing an unbiased statistical analyses. Three levels of relationships were observed in the study. Significant relationships existed between the geomorphometric properties as shown by the correlation network analysis; secondly, individual geomorphometric properties were observed to influence minimum flow discharge; and finally, the multiple regression model set up showed that minimum flow discharge (Q min) was dependent of basin area (AU), stream length (LS), maximum relief (Hmax), average relief (HAV) and stream frequency (SF). These findings further enforced other studies of this nature that drainage basins were dynamic and functional entities whose operations were governed by complex interrelationships occurring within the basins. Changes to any of the geomorphometric properties would influence their role as basin regulators thus influencing a change in basin response. In the case of the basin's minimum low flow, a change in any of the properties considered in the regression model influenced the "time to peak" of flow. A shorter time period would mean higher discharge, which is generally considered the prerequisite to flooding. This research also conclude that the role of geomorphometric properties to control the water supply within the stream through out the year even though during the drought and less precipitations months. Drainage basins are sensitive entities and any deteriorations involve will generate reciprocals and response to the water supply as well as the habitat within the areas.
    Matched MeSH terms: Models, Statistical
  20. Fulltext The effects of age on the strategic use of pitch accents in memory for discourse: a processing-resource account
    Fraundorf SH, Watson DG, Benjamin AS
    Psychol Aging, 2012 Mar;27(1):88-98.
    PMID: 21639646 DOI: 10.1037/a0024138
    In two experiments, we investigated age-related changes in how prosodic pitch accents affect memory. Participants listened to recorded discourses that contained two contrasts between pairs of items (e.g., one story contrasted British scientists with French scientists and Malaysia with Indonesia). The end of each discourse referred to one item from each pair; these references received a pitch accent that either denoted contrast (L + H* in the ToBI system) or did not (H*). A contrastive accent on a particular pair improved later recognition memory equally for young and older adults. However, older adults showed decreased memory if the other pair received a contrastive accent (Experiment 1). Young adults with low working memory performance also showed this penalty (Experiment 2). These results suggest that pitch accents guide processing resources to important information for both older and younger adults but diminish memory for less important information in groups with reduced resources, including older adults.
    Matched MeSH terms: Models, Statistical*
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