An insight of linear regression analysis / Set Foong Ng …[et al.]

Regression models are developed in various field of applications to help researchers to predict certain variables based on other predictor variables. The dependent variables in the regression model are estimated by a number of independent variables. Model utility test is a hypothesis testing procedure in regression to verify if there is a useful relationship between the dependent variable and the independent variable. The hypothesis testing procedure that involves p-value is commonly used in model utility test. A new technique that involves coefficient of determination R2 in model utility test is developed in this paper. The effectiveness of the model utility test in testing the significance of regression model is evaluated using simple linear regression model with the significance level α = 0.01, 0.025 and 0.05. The study in this paper shows that a regression model that is declared to be a significant model by using model utility test, however it fails to guarantee a strong linear relationship between the independent variable and dependent variable. Based on the evaluation presented in this paper, it is shown that the p-value approach in model utility test is not a good technique in evaluating the significance of a regression model. The results of this study could serve as a reference for other researchers applying regression analysis in their studies

Not just how much you know: Interactional effect of cultural knowledge and metacognition on creativity in a global context

ABSTRACTThe ability to think and solve problems creatively in a multicultural environment is critical for success in the 21stcentury. Integrating research on creative cognition and cultural intelligence, we examine the interactional effects of two cognitive capabilities – cultural knowledge and cultural metacognition – on individuals’ creativity in multicultural teams. We propose that although cultural knowledge is useful for creativity, too much knowledge can be detrimental because of cognitive overload and entrenchment. This inverted U-shaped relationship however, is moderated by cultural metacognition. Results of our study support our hypothesis of an inverted U-shape relationship between cultural knowledge and creativity. As expected, we found that the curvilinear effect of cultural knowledge occurs only for individuals with low metacognition. For high cultural metacognition individuals, cultural knowledge has no effect on creativity. These findings offer new insights and practical implications for creativity in today's global environment.</jats:p

Applying the method of Lagrange Multipliers to derive an estimator for unsampled soil properties / Ng Set Foong ... [et al.]

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimized. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained

Assessment of rainfall pattern and future change for Kelantan River Basin, Malaysia using statistically downscaled local climate models

Climate change has been discussed frequently in recent decades, and it has increased the probability of extreme flood occurrence. This study aims to provide an analysis of future rainfall patterns and flood occurrences specifically for the Kelantan River Basin which is identified as one of flood prone areas in Malaysia. The study area was divided into five regions of the Kelantan River Basin,-Kota Bharu (Northern), Kuala Krai (Center), Pos Lebir (Southeastern), Pos Hua (Southwestern) and Pos Gob (Northwestern). The historical rainfall data (1986-2019) was then retrieved from the Malaysian Meteorological Department (MMD) based on the five regions. The statistical approach was applied to downscaled climate model data from the CanESM2 GCM forced by the Representative Concentration Pathway (RCP) 4.5 and 8.5. The reliability assessment using a Cronbach’s Alpha, Linear Regression and Pearson Correlation results show that local climates (2006-2019) forced by RCP4.5 have a similar trend to historical rainfall within the same period. The spatial analysis outcomes showed that the northeastern region of the Kelantan River Basin received its highest average annual rainfall (5,000 mm) in 1990 and caused severe flooding in the area. However, there is a significant change of rainfall pattern in all regions, with a steady increase in annual rainfall in the southwestern region (2021-2100)

Foam and Antifoam Behavior of PDMS in MDEA-PZ Solution in the Presence of Different Degradation Products for CO2 Absorption Process

Absorption is one of the most established techniques to capture CO2 from natural gas and post-combustion processes. Nevertheless, the absorption process frequently suffers from various operational issues, including foaming. The main objective of the current work is to elucidate the effect of degradation product on the foaming behavior in methyldiethanolamine (MDEA) and piperazine (PZ) solution and evaluate the antifoaming performance of polydimethylsiloxane (PDMS) antifoam. The foaming behavior was investigated based on types of degradation product, temperature, and gas flow rate. The presence of glycine, heptanoic acid, hexadecane, and bicine in MDEA-PZ solution cause significant foaming. The presence of hexadecane produced the highest amount of foam, followed by heptanoic acid, glycine and lastly bicine. It was found that increasing the gas flow rate increases foaming tendency and foam stability. Furthermore, increasing temperature increases foaming tendency, but reduces foam stability. Moreover, PDMS antifoam was able to reduce foam formation in the presence of different degradation products and at various temperatures and gas flow rates. It was found that PDMS antifoam works best in the presence of hexadecane with the highest average foam height reduction of 19%. Hence, this work will demonstrate the cause of foaming and the importance of antifoam in reducing its effect

Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well?

The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. It is essential to assess the impacts of the chosen machine learning models using simple anthropometric measurements. We developed and tested 13 machine learning methods of neural network, ensemble, and classical categories to predict hypertension in adolescents using only simple anthropometric measurements. The imbalanced dataset of 2461 samples with 30.1% hypertension subjects was first partitioned into 90% for training and 10% for validation. The training dataset was reduced to eight simple anthropometric measurements: age, C index, ethnicity, gender, height, location, parental hypertension, and waist circumference using correlation coefficient. The Synthetic Minority Oversampling Technique (SMOTE) combined with random under-sampling was used to balance the dataset. The models with optimal hyperparameters were assessed using accuracy, precision, sensitivity, specificity, F1-score, misclassification rate, and AUC on the testing dataset. Across all seven performance measures, no model consistently outperformed the others. LightGBM was the best model for all six performance metrics, except sensitivity, whereas Decision Tree was the worst. We proposed using Bayes’ Theorem to assess the models’ applicability in the Sarawak adolescent population, resulting in the top four models being LightGBM, Random Forest, XGBoost, and CatBoost, and the bottom four models being Logistic Regression, LogitBoost, SVM, and Decision Tree. This study demonstrates that the choice of machine learning models has an effect on the prediction outcomes

Principles and principals: Do customer stewardship and agency control compete or complement when shaping frontline employee behavior?

This article introduces customer stewardship control (CSC) to the marketing field. This concept represents a frontline employee's felt ownership of and moral responsibility for customers' overall welfare. In two studies, the authors show that CSC is a more encompassing construct than customer orientation, which reflects a frontline employee's focus on meeting customers' needs. They provide evidence that the former is more potent in shaping in- and extra-role employee behaviors. Moreover, they highlight how CSC operates in conjunction with an organization's agency control system: Stewardship's positive influence on in- and extra-role behavior is weaker in the presence of high agency control. They offer actionable advice about how to solve the resulting managerial control dilemma. Finally, the authors show that CSC depends on drivers that reside at the individual level (employee relatedness), the team level (team competence), or both levels of aggregation (employee and team autonomy). These findings show how to effectively design a frontline employee's work environment to ensure optimal frontline performance

Differential osteogenic activity of osteoprogenitor cells on HA and TCP/HA scaffold of tissue engineered bone.

Biomaterial, an essential component of tissue engineering, serves as a scaffold for cell attachment, proliferation, and differentiation; provides the three dimensional (3D) structure and, in some applications, the mechanical strength required for the engineered tissue. Both synthetic and naturally occurring calcium phosphate based biomaterial have been used as bone fillers or bone extenders in orthopedic and reconstructive surgeries. This study aims to evaluate two popular calcium phosphate based biomaterial i.e., hydroxyapatite (HA) and tricalcium phosphate/hydroxyapatite (TCP/HA) granules as scaffold materials in bone tissue engineering. In our strategy for constructing tissue engineered bone, human osteoprogenitor cells derived from periosteum were incorporated with human plasma-derived fibrin and seeded onto HA or TCP/HA forming 3D tissue constructs and further maintained in osteogenic medium for 4 weeks to induce osteogenic differentiation. Constructs were subsequently implanted intramuscularly in nude mice for 8 weeks after which mice were euthanized and constructs harvested for evaluation. The differential cell response to the biomaterial (HA or TCP/HA) adopted as scaffold was illustrated by the histology of undecalcified constructs and evaluation using SEM and TEM. Both HA and TCP/HA constructs showed evidence of cell proliferation, calcium deposition, and collagen bundle formation albeit lesser in the former. Our findings demonstrated that TCP/HA is superior between the two in early bone formation and hence is the scaffold material of choice in bone tissue engineering