Displaying publications 41 - 60 of 168 in total

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  1. Fulltext Crystal structure of 1-benzyl-2-hy-droxy-5-oxopyrrolidin-3-yl acetate
    Caracelli I, Zukerman-Schpector J, Stefani HA, Ali B, Tiekink ER
    Acta Crystallogr E Crystallogr Commun, 2015 Aug 1;71(Pt 8):o582-3.
    PMID: 26396808 DOI: 10.1107/S2056989015013353
    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.
    Matched MeSH terms: Least-Squares Analysis
  2. Fulltext An Improved Method of Parameter Identification and Damage Detection in Beam Structures under Flexural Vibration Using Wavelet Multi-Resolution Analysis
    Ravanfar SA, Razak HA, Ismail Z, Monajemi H
    Sensors (Basel), 2015;15(9):22750-75.
    PMID: 26371005 DOI: 10.3390/s150922750
    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.
    Matched MeSH terms: Least-Squares Analysis
  3. Environmental pollution and sustainability of IPO performance: evidence from the Pakistani Market
    Mehmood W, Alsmady AA, Amin S, Mohd-Rashid R, Aman-Ullah A
    Environ Sci Pollut Res Int, 2023 Mar;30(11):30073-30086.
    PMID: 36427127 DOI: 10.1007/s11356-022-23985-8
    This study examined the effect of air pollution on the initial return of IPOs in Pakistan. Cross-sectional data were used to examine 102 listed IPOs on Pakistan Stock Exchange between 1996 and 2019. Ordinary least squares and quantile least squares were employed to examine the influence of air pollution on IPO initial returns. Lastly, stepwise regression was utilised for additional analysis. According to the findings, in the presence of high air pollution, IPO initial returns also increase due to higher uncertainty. The findings demonstrate that air pollution intensifies a company's information environment and financial uncertainty. Therefore, addressing environmental challenges is critical for both public health and capital formation. This study's findings will increase companies' awareness of the economic effect of air pollution, particularly in a country where air pollution is strictly regulated. This study provides businesses with an economic reason to reduce their pollution levels, and it can also help regulators pass environmental laws that are aimed at addressing this issue.
    Matched MeSH terms: Least-Squares Analysis
  4. Fulltext Prediction of the Nitrogen Content of Rice Leaf Using Multi-Spectral Images Based on Hybrid Radial Basis Function Neural Network and Partial Least-Squares Regression
    Wu Y, Al-Jumaili SJ, Al-Jumeily D, Bian H
    Sensors (Basel), 2022 Nov 09;22(22).
    PMID: 36433222 DOI: 10.3390/s22228626
    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.
    Matched MeSH terms: Least-Squares Analysis
  5. Fulltext Rapid investigation of α-glucosidase inhibitory activity of Phaleria macrocarpa extracts using FTIR-ATR based fingerprinting
    Easmin S, Sarker MZI, Ghafoor K, Ferdosh S, Jaffri J, Ali ME, et al.
    J Food Drug Anal, 2017 Apr;25(2):306-315.
    PMID: 28911672 DOI: 10.1016/j.jfda.2016.09.007
    Phaleria macrocarpa, known as "Mahkota Dewa", is a widely used medicinal plant in Malaysia. This study focused on the characterization of α-glucosidase inhibitory activity of P. macrocarpa extracts using Fourier transform infrared spectroscopy (FTIR)-based metabolomics. P. macrocarpa and its extracts contain thousands of compounds having synergistic effect. Generally, their variability exists, and there are many active components in meager amounts. Thus, the conventional measurement methods of a single component for the quality control are time consuming, laborious, expensive, and unreliable. It is of great interest to develop a rapid prediction method for herbal quality control to investigate the α-glucosidase inhibitory activity of P. macrocarpa by multicomponent analyses. In this study, a rapid and simple analytical method was developed using FTIR spectroscopy-based fingerprinting. A total of 36 extracts of different ethanol concentrations were prepared and tested on inhibitory potential and fingerprinted using FTIR spectroscopy, coupled with chemometrics of orthogonal partial least square (OPLS) at the 4000-400 cm-1 frequency region and resolution of 4 cm-1. The OPLS model generated the highest regression coefficient with R2Y = 0.98 and Q2Y = 0.70, lowest root mean square error estimation = 17.17, and root mean square error of cross validation = 57.29. A five-component (1+4+0) predictive model was build up to correlate FTIR spectra with activity, and the responsible functional groups, such as -CH, -NH, -COOH, and -OH, were identified for the bioactivity. A successful multivariate model was constructed using FTIR-attenuated total reflection as a simple and rapid technique to predict the inhibitory activity.
    Matched MeSH terms: Least-Squares Analysis
  6. Fulltext A Hybridised Intelligent Technique for the diagnosis of medical diseases
    Abdu Masanawa Sagir, Saratha Sathasivam
    Pertanika Journal of Science & Technology, 2017;25(2):425-440.
    MyJurnal
    Medical diagnosis is the process of determining which disease or medical condition explains a person’s determinable signs and symptoms. Diagnosis of most diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). It incorporates hybrid learning algorithms least square estimates with Levenberg-Marquardt algorithm using analytic derivation for computation of Jacobian matrix, as well as code optimisation technique, which indexes membership functions. The goal is to investigate how certain diseases are affected by patient’s characteristics and measurement such as abnormalities or a decision about the presence or absence of a disease. In order to achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent technique was tested with Statlog heart disease and Hepatitis disease datasets obtained from the University of California at Irvine’s (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity was examined. In comparison, the proposed method was found to achieve superior
    performance when compared to some other related existing methods.
    Matched MeSH terms: Least-Squares Analysis
  7. Fulltext The development of Adaptive Neuro-Fuzzy Inference System model to diagnosis diabetes disease data set
    Mamuda M, Sathasivam S
    MATEMATIKA, 2017;33(1):11-19.
    MyJurnal
    Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive NeuroFuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.
    Matched MeSH terms: Least-Squares Analysis
  8. Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps
    Lee LC, Liong CY, Jemain AA
    Analyst, 2018 Jul 23;143(15):3526-3539.
    PMID: 29947623 DOI: 10.1039/c8an00599k
    Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.
    Matched MeSH terms: Least-Squares Analysis
  9. Fulltext Simultaneous hyperaccumulation of nickel and cobalt in the tree Glochidion cf. sericeum (Phyllanthaceae): elemental distribution and chemical speciation
    van der Ent A, Mak R, de Jonge MD, Harris HH
    Sci Rep, 2018 Jun 26;8(1):9683.
    PMID: 29946061 DOI: 10.1038/s41598-018-26891-7
    Hyperaccumulation is generally highly specific for a single element, for example nickel (Ni). The recently-discovered hyperaccumulator Glochidion cf. sericeum (Phyllanthaceae) from Malaysia is unusual in that it simultaneously accumulates nickel and cobalt (Co) with up to 1500 μg g-1 foliar of both elements. We set out to determine whether distribution and associated ligands for Ni and Co complexation differ in this species. We postulated that Co hyperaccumulation coincides with Ni hyperaccumulation operating on similar physiological pathways. However, the ostensibly lower tolerance for Co at the cellular level results in the exudation of Co on the leaf surface in the form of lesions. The formation of such lesions is akin to phytotoxicity responses described for manganese (Mn). Hence, in contrast to Ni, which is stored principally inside the foliar epidermal cells, the accumulation response to Co consists of an extracellular mechanism. The chemical speciation of Ni and Co, in terms of the coordinating ligands involved and principal oxidation state, is similar and associated with carboxylic acids (citrate for Ni and tartrate or malate for Co) and the hydrated metal ion. Some oxidation to Co3+, presumably on the surface of leaves after exudation, was observed.
    Matched MeSH terms: Least-Squares Analysis
  10. Fulltext A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique
    Nilashi M, Ahmadi H, Shahmoradi L, Ibrahim O, Akbari E
    J Infect Public Health, 2018 10 04;12(1):13-20.
    PMID: 30293875 DOI: 10.1016/j.jiph.2018.09.009
    BACKGROUND: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning.

    METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies.

    RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine.

    CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.

    Matched MeSH terms: Least-Squares Analysis
  11. The Influence of Organizational External Factors on Construction Risk Management among Nigerian Construction Companies
    Adeleke AQ, Bahaudin AY, Kamaruddeen AM, Bamgbade JA, Salimon MG, Khan MWA, et al.
    Saf Health Work, 2018 Mar;9(1):115-124.
    PMID: 30363069 DOI: 10.1016/j.shaw.2017.05.004
    Background: Substantial empirical research has shown conflicting results regarding the influence of organizational external factors on construction risk management, suggesting the necessity to introduce a moderator into the study. The present research confirmed whether rules and regulations matter on the relationships between organizational external factors and construction risk management.

    Methods: Based on discouragement and organizational control theory, this research examined the effects of organizational external factors and rules and regulations on construction risk management among 238 employees operating in construction companies in Abuja and Lagos, Nigeria. A personally administered questionnaire was used to acquire the data. The data were analyzed using partial least squares structural equation modeling.

    Results: A significant positive relationship between organizational external factors and construction risk management was asserted. This study also found a significant positive relationship between rules and regulations and construction risk management. As anticipated, rules and regulations were found to moderate the relationship between organizational external factors and construction risk management, with a significant positive result. Similarly, a significant interaction effect was also found between rules and regulations and organizational external factors. Implications of the research from a Nigerian point of view have also been discussed.

    Conclusion: Political, economy, and technology factors helped the construction companies to reduce the chance of risk occurrence during the construction activities. Rules and regulations also helped to lessen the rate of accidents involving construction workers as well as the duration of the projects. Similarly, the influence of the organizational external factors with rules and regulations on construction risk management has proven that most of the construction companies that implement the aforementioned factors have the chance to deliver their projects within the stipulated time, cost, and qualities, which can be used as a yardstick to measure a good project.

    Matched MeSH terms: Least-Squares Analysis
  12. Fulltext Patients' perception of the information security management in health centers: the role of organizational and human factors
    Peikari HR, T R, Shah MH, Lo MC
    BMC Med Inform Decis Mak, 2018 Nov 15;18(1):102.
    PMID: 30442138 DOI: 10.1186/s12911-018-0681-z
    BACKGROUND: Researchers paid little attention to understanding the association of organizational and human factors with patients' perceived security in the context of health organizations. This study aims to address numerous gaps in this context. Patients' perceptions about employees' training on security issues, monitoring on security issues, ethics, physical & technical protection and trust in hospitals were identified as organizational and human factors.

    METHODS: After the development of 12 hypotheses, a quantitative, cross-sectional, self-administered survey method was applied to collect data in 9 hospitals in Iran. After the collection of 382 usable questionnaires, the partial least square structural modeling was applied to examine the hypotheses and it was found that 11 hypotheses were empirically supported.

    RESULTS: The results suggest that patients' trust in hospitals can significantly predict their perceived security but no significant associations were found between patients' physical protection mechanisms in the hospital and their perceived information security in a hospital. We also found that patients' perceptions about the physical protection mechanism of a hospital can significantly predict their trust in hospitals which is a novel finding by this research.

    CONCLUSIONS: The findings imply that hospitals should formulate policies to improve patients' perception about such factors, which ultimately lead to their perceived security.

    Matched MeSH terms: Least-Squares Analysis
  13. Convergence in CO 2 emissions, carbon footprint and ecological footprint: evidence from OECD countries
    Solarin SA
    Environ Sci Pollut Res Int, 2019 Feb;26(6):6167-6181.
    PMID: 30617875 DOI: 10.1007/s11356-018-3993-8
    The aim of this paper is to augment the existing literature on convergence of CO2 emissions, by adding carbon footprint per capita and ecological footprint per capita to the convergence debate. We use the residual augmented least squares regression to examine the stochastic convergence of the environmental indices in 27 OECD countries. Furthermore, in contrast to the previous studies which mainly used the conventional beta-convergence approach to examine conditional convergence, we use a beta-convergence method that is capable of identifying the actual number of countries that contribute to conditional convergence. The sigma-convergence of the environmental indices is also examined. The results suggest that conditional convergence exists in 12 countries for CO2 emissions per capita, 15 countries for carbon footprint per capita and also 13 countries for ecological footprint per capita. There is evidence for sigma-convergence for all the three indicators. The policy implications of the results are discussed in the body of the paper.
    Matched MeSH terms: Least-Squares Analysis
  14. The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil
    Rohman A, Che Man YB
    Food Chem, 2011 Nov 15;129(2):583-588.
    PMID: 30634271 DOI: 10.1016/j.foodchem.2011.04.070
    Currently, the authentication of virgin coconut oil (VCO) has become very important due to the possible adulteration of VCO with cheaper plant oils such as corn (CO) and sunflower (SFO) oils. Methods involving Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques (partial least square (PLS) and discriminant analysis (DA)) were developed for quantification and classification of CO and SFO in VCO. MIR spectra of oil samples were recorded at frequency regions of 4000-650cm-1 on horizontal attenuated total reflectance (HATR) attachment of FTIR. DA can successfully classify VCO and that adulterated with CO and SFO using 10 principal components. Furthermore, PLS model correlates the actual and FTIR estimated values of oil adulterants (CO and SFO) with coefficient of determination (R2) of 0.999.
    Matched MeSH terms: Least-Squares Analysis
  15. An Analysis of the World Health Organization Disability Assessment Schedule 2.0 Measurement Model Using Partial Least Squares-Structural Equation Modeling
    Tabet SM, Lambie GW, Jahani S, Rasoolimanesh SM
    Assessment, 2020 12;27(8):1731-1747.
    PMID: 30873844 DOI: 10.1177/1073191119834653
    The researchers examined the factor structure and model specifications of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) with confirmatory tetrad analysis (CTA) using partial least squares-structural equation modeling (PLS-SEM) with a sample of adult clients (N = 298) receiving individual therapy at a university-based counseling research center. The CTA and PLS-SEM results identified the formative nature of the WHODAS 2.0 subscale scores, supporting an alternative measurement model of the WHODAS 2.0 scores as a second-order formative-formative model.
    Matched MeSH terms: Least-Squares Analysis
  16. Predictive modelling of colossal ATR-FTIR spectral data using PLS-DA: empirical differences between PLS1-DA and PLS2-DA algorithms
    Lee LC, Jemain AA
    Analyst, 2019 Apr 08;144(8):2670-2678.
    PMID: 30849143 DOI: 10.1039/c8an02074d
    In response to our review paper [L. C. Lee et al., Analyst, 2018, 143, 3526-3539], we present a study that compares empirical differences between PLS1-DA and PLS2-DA algorithms in modelling a colossal ATR-FTIR spectral dataset. Over the past two decades, partial least squares-discriminant analysis (PLS-DA) has gained wide acceptance and huge popularity in the field of applied research, partly due to its dimensionality reduction capability and ability to handle multicollinear and correlated variables. To solve a K-class problem (K > 2) using PLS-DA and high-dimensional data like infrared spectra, one can construct either K one-versus-all PLS1-DA models or only one PLS2-DA model. The aim of this work is to explore empirical differences between the two PLS-DA algorithms in modeling a colossal ATR-FTIR spectral dataset. The practical task is to build a prediction model using the imbalanced, high dimensional, colossal and multi-class ATR-FTIR spectra of blue gel pen inks. Four different sub-datasets were prepared from the principal dataset by considering the raw and asymmetric least squares (AsLS) preprocessed forms: (a) Raw-global region; (b) Raw-local region; (c) AsLS-global region; and (d) AsLS-local region. A series of 50 models which includes the first 50 PLS components incrementally was constructed repeatedly using the four sub-datasets. Each model was evaluated using six different variants of v-fold cross validation, autoprediction and external testing methods. As a result, each PLS-DA algorithm was represented by a number of figures of merit. The differences between PLS1-DA and PLS2-DA algorithms were assessed using hypothesis tests with respect to model accuracy, stability and fitting. On the other hand, confusion matrices of the two PLS-DA algorithms were inspected carefully for assessment of model parsimony. Overall, both the algorithms presented satisfactory model accuracy and stability. Nonetheless, PLS1-DA models showed significantly higher accuracy rates than PLS2-DA models, whereas PLS2-DA models seem to be much more stable compared to PLS1-DA models. Eventually, PLS2-DA also proved to be less prone to overfitting and is more parsimonious than PLS1-DA. In conclusion, the relatively high accuracy of the PLS1-DA algorithm is achieved at the cost of rather low parsimony and stability, and with an increased risk of overfitting.
    Matched MeSH terms: Least-Squares Analysis
  17. Rapid assessment of total MCPD esters in palm-based cooking oil using ATR-FTIR application and chemometric analysis
    Goh KM, Maulidiani M, Rudiyanto R, Wong YH, Ang MY, Yew WM, et al.
    Talanta, 2019 Jun 01;198:215-223.
    PMID: 30876552 DOI: 10.1016/j.talanta.2019.01.111
    The technique of Fourier transform infrared spectroscopy is widely used to generate spectral data for use in the detection of food contaminants. Monochloropropanediol (MCPD) is a refining process-induced contaminant that is found in palm-based fats and oils. In this study, a chemometric approach was used to evaluate the relationship between the FTIR spectra and the total MCPD content of a palm-based cooking oil. A total of 156 samples were used to develop partial least squares regression (PLSR), artificial neural network (nnet), average artificial neural network (avNNET), random forest (RF) and cubist models. In addition, a consensus approach was used to generate fusion result consisted from all the model mentioned above. All the models were evaluated based on validation performed using training and testing datasets. In addition, the box plot of coefficient of determination (R2), root mean square error (RMSE), slopes and intercepts by 100 times randomization was also compared. Evaluation of performance based on the testing R2 and RMSE suggested that the cubist model predicted total MCPD content with the highest accuracy, followed by the RF, avNNET, nnet and PLSR models. The overfitting tendency was assessed based on differences in R2 and RMSE in the training and testing calibrations. The observations showed that the cubist and avNNET models possessed a certain degree of overfitting. However, the accuracy of these models in predicting the total MCPD content was high. Results of the consensus model showed that it slightly improved the accuracy of prediction as well as significantly reduced its uncertainty. The important variables derived from the cubist and RF models suggested that the wavenumbers corresponding to the MCPDs originated from the -CH=CH2 or CH=CH (990-900 cm-1) and C-Cl stretch (800-700 cm-1) regions of the FTIR spectrum data. In short, chemometrics in combination with FTIR analysis especially for the consensus model represent a potential and flexible technique for estimating the total MCPD content of refined vegetable oils.
    Matched MeSH terms: Least-Squares Analysis
  18. Fulltext Development, validation and translation of cardiopulmonary resuscitation and automated external defibrillator training and placement bilingual questionnaire
    Chew KS, Liaw SY, Ahmad Zahedi AZ, Wong SSL, Singmamae N, Kaushal DN, et al.
    BMC Res Notes, 2019 Oct 21;12(1):670.
    PMID: 31639035 DOI: 10.1186/s13104-019-4698-x
    OBJECTIVES: This paper describes the development and translation of a questionnaire purported to measure (1) the perception of the placement strategy of automated external defibrillator, (2) the perception on the importance of bystander cardiopulmonary resuscitation and automated external defibrillator (3) the perception on the confidence and willingness to apply these two lifesaving interventions as well as (4) the fears and concerns in applying these two interventions. For construct validation, exploratory factor analysis was performed using principal axis factoring and promax oblique rotation and confirmatory factor analysis performed using partial least square.

    RESULTS: Five factors with eigenvalue > 1 were identified. Pattern matrix analysis showed that all items were loaded into the factors with factor loading > 0.4. One item was subsequently removed as Cronbach's alpha > 0.9 which indicates redundancy. Confirmatory factor analysis demonstrated acceptable factor loadings except for one item which was subsequently removed. Internal consistency and discriminant validity was deemed acceptable with no significant cross-loading.

    Matched MeSH terms: Least-Squares Analysis
  19. Fulltext The impact of task characteristics on the performance of nursing teams
    Ahmad Zawawi A, Nasurdin AM
    Int J Nurs Sci, 2017 Jul 10;4(3):285-290.
    PMID: 31406754 DOI: 10.1016/j.ijnss.2017.03.009
    Purpose: This study sought to examine the relationship between team task features and team task performance. Team task performance revolved around the team's technical knowledge and the technical core activities of the organization. On the other hand, team task characteristics include task identity, task significance, and task interdependence.

    Methods: This study involved a total of 300 nursing teams (1436 individual nurses) from seven state hospitals in Peninsular Malaysia. Data were collected using two sets of questionnaires which were initially distributed to 320 teams. One set was given to the team members and another set was given to the team leaders. Of the 320 sets sent out, 300 sets were returned. Responses were then combined and aggregated to the team level to get the team's final score. Analyses of the hypotheses were done using Partial Least Squares (PLS) through assessment of the measurement and structural model.

    Results: Results from the path analysis revealed that of the three dimensions of team task attributes, only task significance was positively and significantly related to team task performance (β = 0.076, P > 0.05), while task identity (β = 0.076, P > 0.05) and task interdependence (β = -0.037, P > 0.05) were found unrelated to team task performance.

    Conclusions: This study demonstrated that task significance is important to predict team task performance. Task significance reflects meaningfulness and nobility of tasks, thus elevate the desire to perform better in each assigned task.

    Matched MeSH terms: Least-Squares Analysis
  20. Fulltext Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry
    Yusop Nurida M, Norfadilah D, Siti Aishah MR, Zhe Phak C, Saleh SM
    Int J Anal Chem, 2020;2020:9830685.
    PMID: 32089691 DOI: 10.1155/2020/9830685
    The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.
    Matched MeSH terms: Least-Squares Analysis
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