Concentrations of primordial radionuclides in common construction materials collected from the south-west coastal region of India were determined using a high-purity germanium gamma-ray spectrometer. Average specific activities (Bq kg(-1)) for (238)U((226)Ra) in cement, brick, soil and stone samples were obtained as 54 ± 13, 21 ± 4, 50 ± 12 and 46 ± 8, respectively. Respective values of (232)Th were obtained as 65 ± 10, 21 ± 3, 58 ± 10 and 57 ± 12. Concentrations of (40)K radionuclide in cement, brick, soil and stone samples were found to be 440 ± 91, 290 ± 20, 380 ± 61 and 432 ± 64, respectively. To evaluate the radiological hazards, radium equivalent activity, various hazard indices, absorbed dose rate and annual effective dose have been calculated, and compared with the literature values. Obtained data could be used as reference information to assess any radiological contamination due to construction materials in future.
Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.
An optimized cultivation condition is needed to maximize the functional green fluorescent protein (GFP) production. Six process variables (agitation rate, temperature, initial medium pH, concentration of inducer, time of induction, and inoculum density) were screened using the fractional factorial design. Three variables (agitation rate, temperature, and time of induction) exerted significant effects on functional GFP production in E. coli shake flask cultivation and were optimized subsequently using the Box-Behnken design. An agitation rate of 206 rpm at 31°C and induction of the protein expression when the cell density (OD(600nm)) reaches 1.04 could enhance the yield of functional GFP production from 0.025 g/L to 0.241 g/L, which is about ninefold higher than the unoptimized conditions. Unoptimized cultivation conditions resulted in protein aggregation and hence reduced the quantity of functional GFP. The model and regression equation based on the shake flask cultivation could be applied to a 2-L bioreactor for maximum functional GFP production.
The newtonian and special-relativistic statistical predictions for the mean, standard deviation and probability density function of the position and momentum are compared for the periodically-delta-kicked particle at low speed. Contrary to expectation, we find that the statistical predictions, which are calculated from the same parameters and initial gaussian ensemble of trajectories, do not always agree if the initial ensemble is sufficiently well-localized in phase space. Moreover, the breakdown of agreement is very fast if the trajectories in the ensemble are chaotic, but very slow if the trajectories in the ensemble are non-chaotic. The breakdown of agreement implies that special-relativistic mechanics must be used, instead of the standard practice of using newtonian mechanics, to correctly calculate the statistical predictions for the dynamics of a low-speed system.
Accurate values are a must in medicine. An important parameter in determining the quality of a medical instrument is agreement with a gold standard. Various statistical methods have been used to test for agreement. Some of these methods have been shown to be inappropriate. This can result in misleading conclusions about the validity of an instrument. The Bland-Altman method is the most popular method judging by the many citations of the article proposing this method. However, the number of citations does not necessarily mean that this method has been applied in agreement research. No previous study has been conducted to look into this. This is the first systematic review to identify statistical methods used to test for agreement of medical instruments. The proportion of various statistical methods found in this review will also reflect the proportion of medical instruments that have been validated using those particular methods in current clinical practice.
The stock market is considered essential for economic growth and expected to contribute to improved productivity. An efficient pricing mechanism of the stock market can be a driving force for channeling savings into profitable investments and thus facilitating optimal allocation of capital. This study investigated the technical efficiency of selected groups of companies of Bangladesh Stock Market that is the Dhaka Stock Exchange (DSE) market, using the stochastic frontier production function approach. For this, the authors considered the Cobb-Douglas Stochastic frontier in which the technical inefficiency effects are defined by a model with two distributional assumptions. Truncated normal and half-normal distributions were used in the model and both time-variant and time-invariant inefficiency effects were estimated. The results reveal that technical efficiency decreased gradually over the reference period and that truncated normal distribution is preferable to half-normal distribution for technical inefficiency effects. The value of technical efficiency was high for the investment group and low for the bank group, as compared with other groups in the DSE market for both distributions in time-varying environment whereas it was high for the investment group but low for the ceramic group as compared with other groups in the DSE market for both distributions in time-invariant situation.
The response surface method was applied in this study to improve cellulase production from oil palm empty fruit bunch (OPEFB) by Botryosphaeria rhodina. An experimental design based on a two-level factorial was employed to screen the significant environmental factors for cellulase production. The locally isolated fungus Botryosphaeria rhodina was cultivated on OPEFB under solid-state fermentation (SSF). From the analysis of variance (ANOVA), the initial moisture content, amount of substrate, and initial pH of nutrient supplied in the SSF system significantly influenced cellulase production. Then the optimization of the variables was done using the response surface method according to central composite design (CCD). Botryosphaeria rhodina exhibited its best performance with a high predicted value of FPase enzyme production (17.95 U/g) when the initial moisture content was at 24.32%, initial pH of nutrient was 5.96, and 3.98 g of substrate was present. The statistical optimization from actual experiment resulted in a significant increment of FPase production from 3.26 to 17.91 U/g (5.49-fold). High cellulase production at low moisture content is a very rare condition for fungi cultured in solid-state fermentation.
Nipah virus (NiV) is a recently emerged zoonotic Paramyxovirus that causes regular outbreaks in East Asia with mortality rate exceeding 75%. Major cellular targets of NiV infection are endothelial cells and neurons. To better understand virus-host interaction, we analyzed the transcriptome profile of NiV infection in primary human umbilical vein endothelial cells. We further assessed some of the obtained results by in vitro and in vivo methods in a hamster model and in brain samples from NiV-infected patients. We found that NiV infection strongly induces genes involved in interferon response in endothelial cells. Among the top ten upregulated genes, we identified the chemokine CXCL10 (interferon-induced protein 10, IP-10), an important chemoattractant involved in the generation of inflammatory immune response and neurotoxicity. In NiV-infected hamsters, which develop pathology similar to what is seen in humans, expression of CXCL10 mRNA was induced in different organs with kinetics that followed NiV replication. Finally, we showed intense staining for CXCL10 in the brain of patients who succumbed to lethal NiV infection during the outbreak in Malaysia, confirming induction of this chemokine in fatal human infections. This study sheds new light on NiV pathogenesis, indicating the role of CXCL10 during the course of infection and suggests that this chemokine may serve as a potential new marker for lethal NiV encephalitis.
Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good in filtering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications.
The pollution status of the downstream section of the Jakara River was investigated. Dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH(3)), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO(3)), chloride (Cl) and phosphates (PO(3-)(4)) were evaluated, using both dry and wet season samples, as a measure of variation in surface water quality in the area. The results obtained from the analyses were correlated using Pearson's correlation matrix, principal component analysis (PCA) and paired sample t-tests. Positive correlations were observed for BOD(5), NH(3), COD, and SS, turbidity, conductivity, salinity, DS, TS for dry and wet seasons, respectively. PCA was used to investigate the origin of each water quality parameter, and yielded 5 varimax factors for each of dry and wet seasons, with 70.7 % and 83.1 % total variance, respectively. A paired sample t-test confirmed that the surface water quality varies significantly between dry and wet season samples (P < 0.01). The source of pollution in the area was concluded to be of anthropogenic origin in the dry season and natural origins in the wet season.
Two statistical tools, Plackett-Burman design (PBD) and Box-Behnken design (BBD) were used to optimize the mycelia growth of Schizophyllum commune with different nutrient components. Results showed that 32.92 g/L of biomass were produced using a medium consisting of 18.74 g/L yeast extract, 38.65 g/L glucose, and 0.59 g/L MgSO(4).7H(2)O. The experimental data fitted well with the model predicted values within 0.09 to 0.77% error. The biomass was also tested for antifungal activity against wood degrading fungi of rubberwood. Results showed that the minimum inhibitory concentration (MIC) values for antifungal activity range from 0.16 to 5.00 μg/μL. The GC-MS analysis indicated that this fungus produced several compounds, such as glycerin, 2(3H)-furanone, 5-heptyldihydro-, 4H-pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl-, and triacetin.
The statistical predictions of Newtonian and special-relativistic mechanics, which are calculated from an initially Gaussian ensemble of trajectories, are compared for a low-speed scattering system. The comparisons are focused on the mean dwell time, transmission and reflection coefficients, and the position and momentum means and standard deviations. We find that the statistical predictions of the two theories do not always agree as conventionally expected. The predictions are close if the scattering is non-chaotic but they are radically different if the scattering is chaotic and the initial ensemble is well localized in phase space. Our result indicates that for low-speed chaotic scattering, special-relativistic mechanics must be used, instead of the standard practice of using Newtonian mechanics, to obtain empirically-correct statistical predictions from an initially well-localized Gaussian ensemble.
The classical procedures of comparing two groups, such as t-test are, usually restricted with the assumptions of normality and equal variances. When these assumptions are violated, the rates of the Type I errors of the independent samples t-test are affected, particularly when the sample sizes are small. In this situation, the bootstrap procedure has an advantage over the parametric t-test. In this study, the performances of the bootstrap procedure and the independent sample t-test were investigated. The investigation focused on the power of both the test procedures to compare the two groups under different design specifications for normal and chi-square distributions. The results showed that the bootstrap procedure has a slight edge over the conventional t-test in term of the rate of achieving the benchmark level for both the distributions. In fact, the bootstrap procedure consistently outperformed the conventional t-test across all the combinations of the test conditions.
Statistical modeling of extreme rainfall is essential since the results can often facilitate civil engineers and planners to estimate the ability of building structures to survive under the utmost extreme conditions. Data comprising of annual maximum series (AMS) of extreme rainfall in Alor Setar were fitted to Generalized Extreme Value (GEV) distribution using method of maximum likelihood (ML) and Bayesian Markov Chain Monte Carlo (MCMC) simulations. The weakness of ML method in handling small sample is hoped to be tackled by means of Bayesian MCMC simulations in this study. In order to obtain the posterior densities, non-informative and independent priors were employed. Performances of parameter estimations were verified by conducting several goodness-of-fit tests. The results showed that Bayesian MCMC method was slightly better than ML method in estimating GEV parameters.
A Poisson model typically is assumed for count data, but when there are so many zeroes in the response variable, because of overdispersion, a negative binomial regression is suggested as a count regression instead of Poisson regression. In this paper, a zero-inflated negative binomial regression model with right truncation count data was developed. In this model, we considered a response variable and one or more than one explanatory variables. The estimation of regression
parameters using the maximum likelihood method was discussed and the goodness-of-fit for the regression model was examined. We studied the effects of truncation in terms of parameters estimation, their standard errors and the goodnessof-fit statistics via real data. The results showed a better fit by using a truncated zero-inflated negative binomial regression model when the response variable has many zeros and it was right truncated.
This article investigates the performance of two-sample pseudo-median based procedure in testing differences between groups. The procedure is a modification of the one-sample Wilcoxon procedure using the pseudo-median of differences between group values as the central measure of location. The test was conducted on two groups with moderate sample
sizes of symmetric and asymmetric distributions. The performance of the procedure was measured in terms of Type I error and power rates computed via Monte Carlo methods. The performance of the procedure was compared against the t-test and Mann-Whitney-Wilcoxon test. The findings from this study revealed that the pseudo-median procedure performed very
well in controlling Type I error rates close to the nominal value. The pseudo-median procedure outperformed the MannWhitney-Wilcoxon test and is comparable to the t-test in controlling Type I error and maintaining adequate power.
This paper focuses on detecting outliers in the circular data which follow the wrapped normal distribution. We considered four discordance tests based on M, C, D and A statistics. The cut-off points of the four tests were obtained and the performance of the detection procedures was studied via simulations. In general, we showed that the discordance test based on the A statistic outperforms the other tests in all cases. For illustration, the city of Kuantan wind direction data set was considered.
Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.
The production of reducing sugars from sago waste via sequential ionic liquid dissolution-solid acid saccharification was optimized in this study. Ionic liquid dissolution of sago waste with 1-butyl-3-methylimidazolium chloride ([BMIM]Cl) was conducted prior to the solid acid saccharification with Amberlyst 15 (A15). The effect of time, temperature and substrate loading during dissolution reaction; and the effect of time, temperature and catalyst loading during saccharification reaction were examined by applying central composite design (CCD) separately. Both dissolution and saccharification reactions were respectively modeled into quadratic polynomial equations with good predictive accuracies. A high reducing sugars yield of 98.3% was obtained under the optimized conditions, i.e. dissolution at 1.75h, 160°C, 1.5% substrate loading, and saccharification at 0.5h, 130°C, 4% catalyst loading. From comparison studies of different saccharification schemes, the sequential ionic liquid dissolution-solid acid saccharification has proven to be a potential method in reducing sugars production from the lignocellulosic biomass.
This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies.