Structural Equation Modelling: Confirmatory Factor Analysis To Construct Measurement Model & Mediator Check Among Formed Factors.

The study aimed to build a measurement model, to describe satisfaction of students towards the quality of service provided at their hostel. A measurement model out of the hypothesized SERVPERF Model, was build for this purpose, using Confirmatory Factor Analysis. A number of 313 respondents were used in this data set. Study found that the hypothesized model with some modifications fits the data well. As the hypothesized model fits the data well, study was also done to investigate if the Working Style factor act as a mediator for the relationship of Empathy factor towards Tangible factor in the modified SERVPERF Model. Study found that Working Style factor act as a partial mediator for this relationship. Keywords: Measurement Model, Confirmatory Factor Analysis, SERVPERF Model, Mediator, Working Style, Empathy, Tangibl

Constructing a MUSA Model to Determine Priority Factor of a SERVPERF Model.

The study aimed to determine the priority factor among the factors in a SERVPERF Model. The SERVPERF Model explains the students’ satisfaction towards the quality of service provided by their hostel management. Priority factor is the factor that is considered important by the customers, but they are not satisfied with the service provided for that factor. A Multi-criteria Satisfaction Analysis (MUSA) Model is built based on ordinal regression with linear programming approach. However, study found that the MUSA Model built is not stable and could not interpret the data set used. This finding is consistent with the fact that MUSA Model does not always give out an interpretable results.. Keywords: Multi-criteria Satisfaction Analysis (MUSA), Modified SERVPERF Model, Priority Factor, Ordinal Regression, Linear Programming Approach

SPATIO-TEMPORAL CORRELATIVE MODELLING OF DENGUE CASES IN SELANGOR, MALAYSIA

This study aims to investigate the relationship of dengue cases with Air Pollution Index (API), climate variable (represented as mean temperature, relative humidity and cumulative rainfall parameters) and spatial effects (represented as neighbouring regions‟ dengue models)

FUNCTIONAL REHABILITATION (LUMBAR SPINE)

Svrha ovog rada je pobliže prikazati lumbalnu kralježnicu te njene moguće deformitete, ozljede, mehanike dobivanja tih ozljeda i oboljenja, te naposljetku funkcionalnu rehabilitaciju kod takvih ozljeda i oboljenja. U početku rada će biti prikazana anatomija same kralježnice,te pobliže objašnjena anatomija lumbalne kralježnice i njenih kralježaka, a kroz daljnji rad ćemo još objasniti kako dolazi do oboljenja, te koje se sve stvari trebaju dogoditi (poklopiti) prije nastanka tih degenerativnih promjena. Na kraju će biti prikazane i objašnjene metode liječenja i rehabilitacije ozljeda i sindroma lumbalne kralježnice.Functional rehabilitation of lumbar spine The purpose of this paper is to closely describe lumbar spine and possible deformities, injuries, mechanics of getting these injuries and illnesses, and ultimately functional rehabilitation in such injuries and illnesses. At the beginning of the paper there will be shown the anatomy of lumbar spine and detailed description of lumbar spine anatomy and its vertebrae. Through further work it will be explained how does it come to illness and which conditions have to be met in order for degenerative changes to happen. The final part of the paper is about treatment and rehabilitation of injuries and syndromes of lumbar spine

Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia

This study investigated the potential relationship between dengue cases and air quality – as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins (BJ) approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were –800.66, – 796.22, and –790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio-temporally based on neighbouring zones, do not have a significant effect on dengue cases