Research examining whether psychological eating style is related to healthy or unhealthy eating patterns is required to explain the mechanisms underlying non-communicable diseases and obesity. The purpose of this study was to investigate whether eating style predicts the nature of food consumption. This was a cross-sectional study of 588 adults (males = 231 and females = 357). Eating style (i.e. restrained, emotional, external eating) was measured using the short version of the Dutch Eating Behaviour Questionnaire (DEBQ). The nature of food consumption was assessed using self-reports of consumption of fruits and vegetables, sweet foods, junk food, and snacks.The results revealed that restrained eating was higher in females and overweight participants. External eating,a higher frequency of snacking,and a higher frequency of junk food consumptionwere more prevalent among the younger participants. Consistent with previous Western studies, emotional eating was found to be the main predictor of consumption of less healthful foods (sweet foods, junk food, and snacks), whereas external eatingpredicted the intake of sweet foods. The intake of fruits and vegetableswas associated with restrained eating. In light of the significant associations between eating style and the nature of food consumption, acknowledging individuals’ eating styleshas implications for tailoring effective nutritional programs that address obesity and the chronic disease epidemic.
The present study is dedicated to analyze the dual-nature solutions of the axisymmetric flow of a magneto-hydrodynamics (MHD) nanofluid over a permeable shrinking sheet. In those phenomena where the fluid flow is due to the shrinking surface, some reverse behaviors of the flow arise because of vorticity effects. Despite of heat transfer analysis, the main purpose of the present study is to attain the solutions of the complex nature problem that appear in reverse flow phenomena. Thermophysical properties of both base fluid (water) and nanoparticles (copper) are also taken into account. By means of similarity transformation, partial differential equations are converted into a system of coupled nonlinear ordinary differential equations and then solved via the Runge-Kutta method. These results are divided separately into two cases: the first one is the unidirectional shrinking along the surface (m = 1) and the other one is for axisymmetric shrinking phenomena (m = 2) . To enhance the thermal conductivity of base fluid, nanoparticle volume fractions ([Formula: see text]) are incorporated within the base fluid. The numerical investigation explores the condition of existence, non-existence and the duality of similarity solution depends upon the range of suction parameter (S) and Hartmann number (M). The reduced skin friction coefficient and local Nusselt number are plotted to analyze the fluid flow and heat transfer at the surface of the shrinking sheet. Streamlines and isotherms are also plotted against the engineering control parameters to analyze the flow behavior and heat transfer within the whole domain. Throughout this analysis it is found that both nanoparticle volume fraction and Hartmann number are increasing functions of both skin friction coefficient and Nusselt number.
Global seaport network efficiency can be measured using the Liner Shipping Connectivity Index (LSCI) with Gross Domestic Product. This paper utilizes k-means and hierarchical strategies by leveraging the results obtained from Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) to cluster 133 countries based on their seaport network efficiency scores. Previous studies have explored hkmeans clustering for traffic, maritime transportation management, swarm optimization, vessel trajectory prediction, vessels behaviours, vehicular ad hoc network etc. However, there remains a notable absence of clustering research specifically addressing the efficiency of global seaport networks. This research proposed hkmeans as the best strategy for the seaport network efficiency clustering where our four newly founded clusters; low connectivity (LC), medium connectivity (MC), high connectivity (HC) and very high connectivity (VHC) are new applications in the field. Using the hkmeans algorithm, 24 countries have been clustered under LC, 47 countries under MC, 40 countries under HC and 22 countries under VHC. With and without a fuzzy dataset distribution, this demonstrates that the hkmeans clustering is consistent and practical to form grouping of general data types. The findings of this research can be useful for researchers, authorities, practitioners and investors in guiding their future analysis, decision and policy makings involving data grouping and prediction especially in the maritime economy and transportation industry.
This study aimed to investigate the association of facial proportion and its relation to the golden ratio with the evaluation of facial appearance among Malaysian population. This was a cross-sectional study with 286 randomly selected from Universiti Sains Malaysia (USM) Health Campus students (150 females and 136 males; 100 Malaysian Chinese, 100 Malaysian Malay and 86 Malaysian Indian), with the mean age of 21.54 ± 1.56 (Age range, 18-25). Facial indices obtained from direct facial measurements were used for the classification of facial shape into short, ideal and long. A validated structured questionnaire was used to assess subjects' evaluation of their own facial appearance. The mean facial indices of Malaysian Indian (MI), Malaysian Chinese (MC) and Malaysian Malay (MM) were 1.59 ± 0.19, 1.57 ± 0.25 and 1.54 ± 0.23 respectively. Only MC showed significant sexual dimorphism in facial index (P = 0.047; P<0.05) but no significant difference was found between races. Out of the 286 subjects, 49 (17.1%) were of ideal facial shape, 156 (54.5%) short and 81 (28.3%) long. The facial evaluation questionnaire showed that MC had the lowest satisfaction with mean score of 2.18 ± 0.97 for overall impression and 2.15 ± 1.04 for facial parts, compared to MM and MI, with mean score of 1.80 ± 0.97 and 1.64 ± 0.74 respectively for overall impression; 1.75 ± 0.95 and 1.70 ± 0.83 respectively for facial parts.
Background Hypertension, or high blood pressure, is a common medical condition that affects a significant portion of the global population. It is a major risk factor for cardiovascular diseases (CVD), stroke, and kidney disorders. Objective The objective of this study is to create and validate a model that combines bootstrapping, ordered logistic regression, and multilayer feed-forward neural networks (MLFFNN) to identify and analyze the factors associated with hypertension patients who also have dyslipidemia. Material and methods A total of 33 participants were enrolled from the Hospital Universiti Sains Malaysia (USM) for this study. In this study, advanced computational statistical modeling techniques were utilized to examine the relationship between hypertension status and several potential predictors. The RStudio (Posit, Boston, MA) software and syntax were implemented to establish the relationship between hypertension status and the predictors. Results The statistical analysis showed that the developed methodology demonstrates good model fitting through the value of predicted mean square error (MSE), mean absolute deviance (MAD), and accuracy. To evaluate model fitting, the data in this study was divided into distinct training and testing datasets. The findings revealed that the results strongly support the superior predictive capability of the hybrid model technique. In this case, five variables are considered: marital status, smoking status, systolic blood pressure, fasting blood sugar levels, and high-density lipoprotein levels. It is important to note that all of them affect the hazard ratio: marital status (β1, -17.12343343; p < 0.25), smoking status (β2, 1.86069121; p < 0.25), systolic blood pressure (β3, 0.05037332; p < 0.25), fasting blood sugar (β4, -0.53880322; p < 0.25), and high-density lipoprotein (β5, 5.38065556; p < 0.25). Conclusion This research aims to develop and extensively evaluate the hybrid approach. The statistical methods employed in this study using R language show that regression modeling surpasses R-squared values in predicting the mean square error. The study's conclusion provides strong evidence for the superiority of the hybrid model technique.