This paper reports on a two-step approach for optimally determining the location and severity of damage in beam structures under flexural vibration. The first step focuses on damage location detection. This is done by defining the damage index called relative wavelet packet entropy (RWPE). The damage severities of the model in terms of loss of stiffness are assessed in the second step using the inverse solution of equations of motion of a structural system in the wavelet domain. For this purpose, the connection coefficient of the scaling function to convert the equations of motion in the time domain into the wavelet domain is applied. Subsequently, the dominant components based on the relative energies of the wavelet packet transform (WPT) components of the acceleration responses are defined. To obtain the best estimation of the stiffness parameters of the model, the least squares error minimization is used iteratively over the dominant components. Then, the severity of the damage is evaluated by comparing the stiffness parameters of the identified model before and after the occurrence of damage. The numerical and experimental results demonstrate that the proposed method is robust and effective for the determination of damage location and accurate estimation of the loss in stiffness due to damage.
Partial least squares discriminant analysis (PLS-DA) is a well-known technique for feature extraction and discriminant analysis in chemometrics. Despite its popularity, it has been observed that PLS-DA does not automatically lead to extraction of relevant features. Feature learning and extraction depends on how well the discriminant subspace is captured. In this paper, discriminant subspace learning of chemical data is discussed from the perspective of PLS-DA and a recent extension of PLS-DA, which is known as the locality preserving partial least squares discriminant analysis (LPPLS-DA). The objective is twofold: (a) to introduce the LPPLS-DA algorithm to the chemometrics community and (b) to demonstrate the superior discrimination capabilities of LPPLS-DA and how it can be a powerful alternative to PLS-DA. Four chemical data sets are used: three spectroscopic data sets and one that contains compositional data. Comparative performances are measured based on discrimination and classification of these data sets. To compare the classification performances, the data samples are projected onto the PLS-DA and LPPLS-DA subspaces, and classification of the projected samples into one of the different groups (classes) is done using the nearest-neighbor classifier. We also compare the two techniques in data visualization (discrimination) task. The ability of LPPLS-DA to group samples from the same class while at the same time maximizing the between-class separation is clearly shown in our results. In comparison with PLS-DA, separation of data in the projected LPPLS-DA subspace is more well defined.
Perceived usefulness and perceived ease of use constitute important belief factors when technology adoption decisions are made within a non-mandatory setting. This paper investigated the role played by psychological ownership in shaping teachers' beliefs about using a cloud-based virtual learning environment (VLE). Psychological ownership is increasingly becoming a relevant phenomenon in technology adoption research, where people can feel psychologically attached to a particular technology. The study proposed that such phenomenon can also occur when using a VLE, and a hypothesised model with six constructs was tested with 629 Malaysian teachers from 21 schools. Results from structural equation modelling-partial least squares analysis found teachers' experiences with the VLE significantly influenced psychological ownership, which in turn significantly predicted perceived usefulness and perceived ease of use of the VLE. Overall, the model possesses predictive relevance for the outcome predictors as indicated by Stone-Geisser's Q2, and accounted for 61.6% of variance in perceived usefulness and 62.0% of variance in perceived ease of use. This study provides insights into the motivation behind teachers' beliefs which are shaped by their experiences with the VLE. Implications for theory and practice were discussed based on the insights of the study.
Previous studies have indicated that the pipe-surface-mounted helical strakes effectively reduce vortex-induced vibration (VIV) under a uniform flow application, particularly during the lock-in region. Since VIV experiments are time-consuming, observation is generated with an interval helical strakes parameter in pitch and height to lessen tedious procedures and repetitive post-processing analyses. The aforementioned result subset is insufficient for helical strakes design optimisation because the trade-off between the helical strakes dimension, lock-in region and flow velocity are non-trivial. Thus, a parametric model based on an improved recursive least squares (RLS) parameter estimation technique is proposed to define the statistical relationship between input, or strakes and pipe dimension, and output, or VIV amplitude ratio. As results suggested, revised RLS estimated VIV model demonstrated an optimal prediction with the highest coefficient of determination and lowest Integral Absolute Error. The feasibility of VIV parametric model was validated by embed into Genetic Algorithm (GA) as the fitness function to acquire a desirable helical strakes dimension with minimum VIV amplitude. The rapid generation of optimal helical strakes dimension which returned the highest VIV suppression implied a superior simulation method compared to the experimental outcome.
The purpose of the present study was to examine the relationship among spiritual intelligence, spiritual well-being and death anxiety among Iranian veterans. In this predictive correlational study, 211 veterans completed King and DeCicco's Spiritual Intelligence Scale, Paloutzian and Ellison's Spiritual Well-being Scale and Templer's Death Anxiety Scale-Extended. After confirming the reliability of the constructs using intra-class correlation coefficient, partial least squares structural equation modeling method was utilized to assess the impact of spiritual well-being and spiritual intelligence on death anxiety. This study found a significant positive relationship between spiritual intelligence and death anxiety after controlling for the effects of age, education level and disability. However, there was a significant negative relationship between spiritual well-being and death anxiety among Iranian veterans. Negative relationships were found between spiritual well-being and death anxiety among Iranian veterans. However, spiritual intelligence had a positive impact on death anxiety.
In the title compound, C13H15NO4, the oxopyrrolidin-3-yl ring has an envelope conformation, with the C atom bearing the acetate group being the flap. The acetate and phenyl groups are inclined with respect to the central ring, forming dihedral angles of 50.20 (12) and 87.40 (9)°, respectively, with the least-squares plane through the ring. The dihedral angle between the acetate group and the phenyl ring is 63.22 (8)°, indicating a twisted conformation in the mol-ecule. In the crystal, supra-molecular chains along the b axis are formed by (hy-droxy)O-H⋯O(ring carbon-yl) hydrogen bonds. The chains are consolidated into the three-dimensional architecture by C-H⋯O inter-actions.
Blood flow through a bifurcated artery with the presence of an overlapping stenosis located at parent’s arterial lumen under the action of a uniform external magnetic field is studied in this paper. Blood is treated as an electrically conducting fluid which exhibits the Magnetohydrodynamics principle and it is characterized by a Newtonian fluid model. The governing equations are discretized using a stabilization technique of finite element known as Galerkin least-squares. The maximum velocity and pressure drop evaluated in this present study are compared with the results found in previous literature and COMSOL Multiphysics. The solutions found in a satisfactory agreement, thus verify the source code is working properly. The effects of dimensionless parameters of Hartmann and Reynolds numbers in the fluid’s velocity and pressure are examined in details with further scientific discussions.
Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data were collected from the year 1980 until 2014 from the website of the Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved using the least square method. Since least square problem is an unconstrained optimisation, the Conjugate Gradient (CG) was chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables. Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value.
Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.
The objective of this study is to investigate the demand for quality vegetables in Malaysia. This study estimates quality elasticities from the difference between expenditure and quantity elasticities in order to show the demand for quality vegetables in Malaysia. By using the Household Expenditure Survey 2004/2005, expenditure and quantity Engel equations are estimated via two stage least square. The positive estimated quality elasticities (except root and tuberous vegetable) show that Malaysian consumers tend to increase their demand for quality vegetables in response to their incomes rise. To be more specific, urban consumers are expected to demand more of higher quality vegetables (except root and tuberous vegetable) than rural consumers.
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.
Annual air temperature data obtained from twenty-two meteorological stations across Malaysia are modeled using multiple regression. A correlation test was conducted to find statistical relationship between each of the dependent variables: annual maximum and annual average air temperature and predictor variables: longitude, latitude, elevation and wind speed. Regression models using least square estimation method were developed relating the dependent variables to independent variables and the adequacy of the models is determined by the coefficient of determination. The result shows that the longitude and wind speed factors have a significant influence on the annual air temperature in Malaysia.
The growth of microorganism on substrates, whether toxic or not usually exhibits sigmoidal
pattern. This sigmoidal growth pattern can be modelled using primary models such as Logistic,
modified Gompertz, Richards, Schnute, Baranyi-Roberts, Von Bertalanffy, Buchanan threephase
and Huang. Previously, the modified Gompertz model was chosen to model the growth of
Burkholderia sp. strain Neni-11 on acrylamide, which shows a sigmoidal curve. The modified
Gompertz model relies on the ordinary least squares method, which in turn relies heavily on
several important assumptions, which include that the data does not show autocorrelation. In this
work we perform statistical diagnosis test to test for the presence of autocorrelation using the
Durbin-Watson test and found that the model was adequate and robust as no autocorrelation of
the data was found.
One of the most important lifetime distributions that is used for modelling and analysing data in clinical, life sciences and engineering is the Weibull distribution. The main objective of this paper was to determine the best estimator for the two-parameter Weibull distribution. The methods under consideration are the frequentist maximum likelihood estimator, least square regression estimator and the Bayesian estimator by using two loss functions, which are squared error and linear exponential. Lindley approximation is used to obtain the Bayes estimates. Comparisons are made through simulation study to determine the performance of these methods. Based on the results obtained from this simulation study the Bayesian approach used in estimating the Weibull parameters under linear exponential loss function is found to be superior as compared to the conventional maximum likelihood and least squared methods.
A unit root test based on the modified least squares (MLS) estimator for first-order autoregressive process is proposed and compared with unit root tests based on the ordinary least squares (OLS), the weighted symmetric (WS) and the modified weighted symmetric (MWS) estimators. The percentiles of the null distributions of the unit root test are also reported. The empirical probabilities of type I error and powers of the unit root tests were estimated via Monte Carlo simulation. The simulation results showed that all unit root tests can control the probability of type I error for all situations. The empirical power of the test is higher than the other unit root tests, and Apart from that, the and tests also provide the highest empirical power. As an illustration, the monthly series of U.S. nominal interest rates on three-month treasury bills is analyzed.
The study was conducted to determine the best model suitable for the determination of ferrum uptake in Brassica chinensis (flowering white cabbage). A nonlinear regression model was selected to determine the amount of ferrum absorbed by each part of the Brassica chinensis plant namely the leaves, stems and roots. The Levenberg-Marquardt method was used to perform the nonlinear least square fit. This method employs information on the gradients and hence requires specification of the partial derivatives. A suitable model was obtained from the exponential regression model. The polynomial model was found to be appropriate for leaves, the mono-exponential model was suitable for stems and the simple exponential model for roots. The residual plots and the normal probability plots from each of the models indicated no substantial diagnostic problems, so it can be concluded that the polynomial and exponential regression models provide adequate fit to determine data on heavy metal uptake by the flowering white cabbage.
The paper proposes and validates the strategic public procurement regulatory compliance model with mediation effect of ethical behavior. It expands the socio-economic theory of regulatory compliance to explore the mediating effect of ethical behavior on the influence of professionalism, familiarity, enforcement, resistance to political pressure and compliance with public procurement regulation. A quantitative research design was deployed using 125 procurement officers as a sample group. The data from the sample was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results validated the hypotheses for the strategic public procurement regulatory compliance model with mediation effect of ethical behavior. The study not only confirmed the earlier findings on the direct effects of professionalism, familiarity, enforcement, resistance to political pressure and ethical behavior on compliance, but also established the mediating effect of ethical behavior on compliance on all the predictors except resistance to political pressure. The study implied that ethical behavior of public procurement officers should be a strategic point of concern by both policymakers and professional bodies. Theoretically, the studyexpands thesocio-economic theory of regulatory compliance within the context of procurement literature through mediation effects of ethical behavior via structural analysis.
This paper's novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively.
186 Re (T1/2= 89.24 h, [Formula: see text] 346.7 keV, [Formula: see text] ), an intense beta-emitter shows great potential to be used as an active material in therapeutic radiopharmaceuticals due to its suitable physico-chemical properties.186Re can be produced in several ways, however charged-particle induced reactions show to be promising towards no carrier added production. In this work, production cross-sections of186Re were evaluated following the light-charged particle induced reactions on tungsten. An effective evaluation technique such as Simultaneous Evaluation on KALMAN code combined with least squares concept was used to obtain the evaluated data together with covariances. Knowledge of the underlying uncertainties in evaluated nuclear data, i.e., covariances are useful to improve the accuracy of nuclear data.
Spectral data is often required to be pre-processed prior to applying a multivariate modelling technique. Baseline correction of spectral data is one of the most important and frequently applied pre-processing procedures. This preliminary study aims to investigate the impacts of six types of baseline correction algorithms on classifying 150 infrared spectral data of three varieties of paper. The algorithms investigated were Iterative Restricted Least Squares, Asymmetric Least Squares (ALS), Low-pass FFT Filter, Median Window (MW), Fill Peaks and Modified Polynomial Fitting. Processed spectral data were then analysed using Principal Component Analysis (PCA) to visually examine the clustering among the three varieties of paper. Results show that separation among the three varieties of paper is greatly improved after baseline correction via ALS, FP and MW algorithms.