The purpose of this study is to measure and monitor the radon concentration from fabricated foamed light concrete, made of Portland cement, mine sand and granite. The concentration of radon released was measured using Radon Monitor Model 1027 from Sun Nuclear. The results of this research showed that the avearge radon concentration from foamed light concrete was 2.2 pCiL-1 L. Higher radon concentrations were detected after three days of measurements. Environment Protection Agency stated in its guidelines that radon concentration must lower than 4 pCiL-1 for a healthy environment. Thus, the use of foamed light concrete can be one of the alternatives to reduce radon concentration levels in human environment.
The present work is concerned with exact solutions of Stokes second problem for magnetohydrodynamics (MHD) flow of a Burgers' fluid. The fluid over a flat plate is assumed to be electrically conducting in the presence of a uniform magnetic field applied in outward transverse direction to the flow. The equations governing the flow are modeled and then solved using the Laplace transform technique. The expressions of velocity field and tangential stress are developed when the relaxation time satisfies the condition γ = λ²/4 or γ> λ²/4. The obtained closed form solutions are presented in the form of simple or multiple integrals in terms of Bessel functions and terms with only Bessel functions. The numerical integration is performed and the graphical results are displayed for the involved flow parameters. It is found that the velocity decreases whereas the shear stress increases when the Hartmann number is increased. The solutions corresponding to the Stokes' first problem for hydrodynamic Burgers' fluids are obtained as limiting cases of the present solutions. Similar solutions for Stokes' second problem of hydrodynamic Burgers' fluids and those for Newtonian and Oldroyd-B fluids can also be obtained as limiting cases of these solutions.
One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.
The objective of this paper is to apply the Translog Stochastic Frontier production model (SFA) and Data Envelopment Analysis (DEA) to estimate efficiencies over time and the Total Factor Productivity (TFP) growth rate for Bangladeshi rice crops (Aus, Aman and Boro) throughout the most recent data available comprising the period 1989-2008. Results indicate that technical efficiency was observed as higher for Boro among the three types of rice, but the overall technical efficiency of rice production was found around 50%. Although positive changes exist in TFP for the sample analyzed, the average growth rate of TFP for rice production was estimated at almost the same levels for both Translog SFA with half normal distribution and DEA. Estimated TFP from SFA is forecasted with ARIMA (2, 0, 0) model. ARIMA (1, 0, 0) model is used to forecast TFP of Aman from DEA estimation.
Steel pallet rack (SPR) beam-to-column connections (BCCs) are largely responsible to avoid the sway failure of frames in the down-aisle direction. The overall geometry of beam end connectors commercially used in SPR BCCs is different and does not allow a generalized analytic approach for all types of beam end connectors; however, identifying the effects of the configuration, profile and sizes of the connection components could be the suitable approach for the practical design engineers in order to predict the generalized behavior of any SPR BCC. This paper describes the experimental behavior of SPR BCCs tested using a double cantilever test set-up. Eight sets of specimens were identified based on the variation in column thickness, beam depth and number of tabs in the beam end connector in order to investigate the most influential factors affecting the connection performance. Four tests were repeatedly performed for each set to bring uniformity to the results taking the total number of tests to thirty-two. The moment-rotation (M-θ) behavior, load-strain relationship, major failure modes and the influence of selected parameters on connection performance were investigated. A comparative study to calculate the connection stiffness was carried out using the initial stiffness method, the slope to half-ultimate moment method and the equal area method. In order to find out the more appropriate method, the mean stiffness of all the tested connections and the variance in values of mean stiffness according to all three methods were calculated. The calculation of connection stiffness by means of the initial stiffness method is considered to overestimate the values when compared to the other two methods. The equal area method provided more consistent values of stiffness and lowest variance in the data set as compared to the other two methods.
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.
A statistical prediction of terrestrial gamma radiation dose rate has been performed, covering the Kota Tinggi district of Peninsular Malaysia. The prediction has been based on geological features and soil types. The purpose of this study is to provide a methodology to statistically predict the gamma radiation dose rate with minimum surveying in an area. Results of statistical predictions using the hypothesis test were compared with the actual dose rate obtained by measurements.
In the biomechanics field, material parameters calibration is significant for finite element (FE) model to ensure a legit estimation of biomechanical response. Determining an appropriate combination of calibration factors is challenging as each constitutive component responds differently. This study proposes a statistical factorial analysis approach using L16(4(5)) orthogonal array to evaluate material nonlinearity and applicable calibration factor of the intervertebral disc FE model in pure moment. The calibrated model exhibits improved agreement to the experimental findings for all directions. Appropriate combination of calibration parameter reduces the estimation gap to the experimental findings, ensuring agreeable biomechanical responses.
In this study, the combination of parameters required for optimal extraction of anti-oxidative components from the Chinese lotus (CLR) and Malaysian lotus (MLR) roots were carefully investigated. Box-Behnken design was employed to optimize the pH (X1: 2-3), extraction time (X2: 0.5-1.5 h) and solvent-to-sample ratio (X3: 20-40 mL/g) to obtain a high flavonoid yield with high % DPPHsc free radical scavenging and Ferric-reducing power assay (FRAP). The analysis of variance clearly showed the significant contribution of quadratic model for all responses. The optimal conditions for both Chinese lotus (CLR) and Malaysian lotus (MLR) roots were obtained as: CLR: X1 = 2.5; X2 = 0.5 h; X3 = 40 mL/g; MLR: X1 = 2.4; X2 = 0.5 h; X3 = 40 mL/g. These optimum conditions gave (a) Total flavonoid content (TFC) of 0.599 mg PCE/g sample and 0.549 mg PCE/g sample, respectively; (b) % DPPHsc of 48.36% and 29.11%, respectively; (c) FRAP value of 2.07 mM FeSO4 and 1.89 mM FeSO4, respectively. A close agreement between predicted and experimental values was found. The result obtained succinctly revealed that the Chinese lotus exhibited higher antioxidant and total flavonoid content when compared with the Malaysia lotus root at optimum extraction condition.
Hydrolyzing the amorphous region while keeping the crystalline region unaltered is the key technology for producing nanocellulose. This study investigated if the dissolution properties of the amorphous region of microcrystalline cellulose can be enhanced in the presence of Fe(3+) salt in acidic medium. The process parameters, including temperature, time and the concentration of metal chloride catalyst (FeCl₃), were optimized by using the response surface methodology (RSM). The experimental observation demonstrated that temperature and time play vital roles in hydrolyzing the amorphous sections of cellulose. This would yield hydrocellulose with higher crystallinity. The factors that were varied for the production of hydrocellulose were the temperature (x₁), time (x₂) and FeCl₃ catalyst concentration (x₃). Responses were measured in terms of percentage of crystallinity (y₁) and the yield (y₂) of the prepared hydrocellulose. Relevant mathematical models were developed. Analysis of variance (ANOVA) was carried out to obtain the most significant factors influencing the responses of the percentage of crystallinity and yield. Under optimum conditions, the percentage of crystallinity and yield were 83.46% and 86.98% respectively, at 90.95 °C, 6 h, with a catalyst concentration of 1 M. The physiochemical characteristics of the prepared hydrocellulose were determined in terms of XRD, SEM, TGA and FTIR analyses. The addition of FeCl₃ salt in acid hydrolyzing medium is a novel technique for substantially increasing crystallinity with a significant morphological change.
Biocatalytic reaction is a type of reaction which uses enzyme or whole-cell as a (bio)-catalyst to achieve a desired conversion, under controlled conditions in a bioreactor. Temperature produces opposed effects on enzyme activity and stability, and is therefore a key variable in any biocatalytic processes. An exothermic biocatalytic reaction, in a continuous-stirred-tank reactor (CSTR), was analyzed where dynamic equations (non-linear differential equations) could be derived from the Michaelis-Menten and Arrhenius equations, by performing mass and energy balances on the reactor. In this work, the effects of the different parameters such as dilution rate, proportional control constant and dimensionless total enzyme concentration, on the stability of the system, were studied. The stability of the reaction could be analyzed, based on the ODE (ordinary differential equation), solved using the numerical technique in MATLAB® and the analytical investigation using Mathematica.® The numerical analysis can be carried out by considering the hase-plane behaviour and bifurcation diagrams of the dynamic equations, while the analytical analysis using Mathematica® can be undertaken by evaluating the eigenvalues of the system. In order to model the operational stability of biocatalysts, modulation factors need to be considered so that a proper design of bioreactors can be done. Temperature, as a key variable in such bioprocess systems, can be conveniently optimized through the use of appropriate models.
Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%.
Seagrass species in the tropics occur in multispecies meadows. How these meadows are maintained through species co-existence and what their ecological drivers may be has been an overarching question in seagrass biogeography. In this study, we quantify the spatial structure of four co-existing species and infer potential ecological processes from these structures.
Modelling observed meteorological elements can be useful. For instance, modelling rainfall has
been an interest for many researchers. In a previous research, trend surface analysis was used and
it was indicated that the residuals might spatially be correlated. When dealing with spatial data, any
modelling technique should take spatial correlation into consideration. Hence, in this project, fitting
of spatial regression models, with spatially correlated errors to the annual mean relative humidity
observed in Peninsular Malaysia, is illustrated. The data used in this study comprised of the annual
mean relative humidity for the year 2000-2004, observed at twenty principal meteorological stations
distributed throughout Peninsular Malaysia. The modelling process was done using the S-plus
Spatial Statistics Module. A total of twelve models were considered in this study and the selection
of the model was based on the p-value. It was found that a possible appropriate model for the
annual mean relative humidity should include an intercept and a term of the longitude as covariate,
together with a conditional autoregressive error structure. The significance of the coefficient of the
covariate and spatial parameter was established using the Likelihood Ratio Test. The usefulness
of the proposed model is that it could be used to estimate the annual mean relative humidity at
places where observations were not recorded and also for prediction. Some other potential models
incorporating the latitude covariate have also been proposed as viable alternatives.
Proper implementation of landfill siting with the right regulations and constraints can prevent undesirable long-term effects. Different countries have respective guidelines on criteria for new landfill sites. In this article, we perform a comparative study of municipal solid waste landfill siting criteria stated in the policies and guidelines of eight different constitutional bodies from Malaysia, Australia, India, U.S.A., Europe, China and the Middle East, and the World Bank. Subsequently, a geographic information system (GIS) multi-criteria evaluation model was applied to determine new suitable landfill sites using different criterion parameters using a constraint mapping technique and weighted linear combination. Application of Macro Modeler provided in the GIS-IDRISI Andes software helps in building and executing multi-step models. In addition, the analytic hierarchy process technique was included to determine the criterion weight of the decision maker's preferences as part of the weighted linear combination procedure. The differences in spatial results of suitable sites obtained signifies that dissimilarity in guideline specifications and requirements will have an effect on the decision-making process.
Simultaneous removal of SO2 and NO from simulated flue gas by cerium oxide supported over palm shell activated carbon (Ce/PSAC) was studied in a fixed bed adsorber. In this study, the adsorption breakthrough of SO2 and NO on Ce/PSAC at different reaction temperatures was manipulated to test their applicability to a model developed by Yoon and Nelson (1984) for breakthrough curves. Yoon and Nelson (1984) developed a relatively simple model addressing the adsorption and breakthrough of adsorbate vapour with respect to activated charcoal. This model was based on the assumption that the rate of decrease in the probability of adsorption for each adsorbate molecule is proportional to the probability of adsorbate adsorption and the probability of adsorbate breakthrough on the adsorbent. A regression analysis (least square method) has been used to give the model parameters of k and t1/2. The results showed that the agreement between the model and the experimental results is satisfactory. From the observation, it is concluded that the simple two-parameter model of Yoon and Nelson’s model can be applied for modelling the breakthrough curves of SO2 and NO gas adsorption over Ce/PSAC.
A mathematical model is considered to determine the effectiveness of disin-
fectant solution for surface decontamination. The decontamination process involved the
diffusion of bacteria into disinfectant solution and the reaction of the disinfectant killing
effect. The mathematical model is a reaction-diffusion type. Finite difference method and
method of lines with fourth-order Runge-Kutta method are utilized to solve the model
numerically. To obtain stable solutions, von Neumann stability analysis is employed to
evaluate the stability of finite difference method. For stiff problem, Dormand-Prince
method is applied as the estimated error of fourth-order Runge-Kutta method. MATLAB
programming is selected for the computation of numerical solutions. From the results
obtained, fourth-order Runge-Kutta method has a larger stability region and better ac-
curacy of solutions compared to finite difference method when solving the disinfectant
solution model. Moreover, a numerical simulation is carried out to investigate the effect
of different thickness of disinfectant solution on bacteria reduction. Results show that
thick disinfectant solution is able to reduce the dimensionless bacteria concentration more
effectively.
This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic algorithm that is derived from careful observation of the African buffalos, a species of wild cows, in the African forests and savannahs. This animal displays uncommon intelligence, strategic organizational skills, and exceptional navigational ingenuity in its traversal of the African landscape in search for food. The African Buffalo Optimization builds a mathematical model from the behavior of this animal and uses the model to solve 33 benchmark symmetric Traveling Salesman's Problem and six difficult asymmetric instances from the TSPLIB. This study shows that buffalos are able to ensure excellent exploration and exploitation of the search space through regular communication, cooperation, and good memory of its previous personal exploits as well as tapping from the herd's collective exploits. The results obtained by using the ABO to solve these TSP cases were benchmarked against the results obtained by using other popular algorithms. The results obtained using the African Buffalo Optimization algorithm are very competitive.
We study a fuzzy fractional differential equation (FFDE) and present its solution using Zadeh's extension principle. The proposed study extends the case of fuzzy differential equations of integer order. We also propose a numerical method to approximate the solution of FFDEs. To solve nonlinear problems, the proposed numerical method is then incorporated into an unconstrained optimisation technique. Several numerical examples are provided.
Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R² were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R² = 0.98.