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.
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.
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.
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.
OBJECTIVE: To determine and quantify lard as an adulterant in a binary blend with palm oil in a cosmetic soap formulations by FT-IR and multivariate analysis.
METHODS: Fatty acids in lard, palm oil and binary blends were extracted via liquid-liquid extraction and were subjected to FTIR spectrometry, combined with principal component analysis (PCA) and discriminant analysis (DA) for the classification of lard in cosmetic soap formulations via two DA models: Model A (percentage of lard in cosmetic soap) and Model B (porcine and non-porcine cosmetic soap). Linear regression (MLR), partial least square regression (PLS-R) and principal components regression (PCR) were used to assess the degree of adulteration of lard in the cosmetic soap.
FINDINGS: The FTIR spectrum of palm oil slightly differed from that of lard at the wavenumber range of 1453 cm -1 and 1415 cm -1 in palm oil and lard, respectively, indicating the bending vibrations of CH2 and CH3 aliphatic groups and OH carboxyl group respectively. Both of the DA models could accurately classify 100% of cosmetic soap formulations. Nevertheless, less than 100% of verification value was obtained when it was further used to predict the unknown cosmetic soap sample suspected of containing lard or a different percentage of lard. The PCA for Model A and Model B explained a similar cumulative variability (CV) of 92.86% for the whole dataset. MLR and PCR showed the highest determination coefficient (R2) of 0.996, and the lowest relative standard error (RSE) and mean square error (MSE), indicating that both regression models were effective in quantifying the lard adulterant in cosmetic soap.
CONCLUSION: FTIR spectroscopy coupled with chemometrics with DA, PCA and MLR or PCR can be used to analyse the presence of lard and quantify its percentage in cosmetic soap formulations.