The predictors to medication adherence among adults with diabetes in the United Arab Emirates.

BackgroundDiabetes is a chronic medical condition and adherence to medication in adults with diabetes is important. Identifying predictors to medication adherence in adults with diabetes would help identify vulnerable patients who are likely to benefit by improving their adherence levels.MethodsWe conducted a cross-sectional study at the Dubai Police Health Centre between February 2015 and November 2015. Questionnaires were used to collect socio-demographic, clinical and disease related variables and the primary measure of outcome was adherence levels as measured by the Morisky Medication Adherence Scale (MMAS-8©). Multivariate logistic regression was carried out to identify predictors to adherence.ResultsFour hundred and forty six patients were interviewed. Mean age 61 year +/- 11. 48.4 % were male. The mean time since diagnosis of diabetes was 3.2 years (Range 1-15 years). Two hundred and eighty eight (64.6 %) patients were considered non-adherent (MMAS-8© adherence score < 6) while 118 (26.5 %) had moderate adherence (MMAS-8© adherence score 6 = <8) and 40 (9.0 %) high adherence (MMAS-8© adherence scores <8) to their medication respectively. The strongest predictor for adherence as predicted by the multi-logistic regression model was the patient's level of education. A technical diploma certificate as compared to a primary school level of education was the strongest predictor of adherence (OR = 66.1 CI: 6.93 to 630.43); p < 0.001). The patient's age was also a predictor of adherence with older patients reporting higher levels of adherence (OR = 1.113 (CI: 1.045 to 1.185; p = 0.001 for every year increase in age). The duration of diabetes was also a predictor of adherence (OR = 1.830 (CI: 1.270 to 2.636; p = 0.001 for every year increase in the duration of diabetes). Other predictors to medication adherence include Insulin use, ethnicity and certain cultural behaviours.ConclusionA number of important predictors to medication adherence in diabetics were identified in this study. Such predictors could help develop policies for improving adherence in diabetics

The Effects Of Task-Technology Fit On Use And User Performance Impacts: The Case Of The Human Resource Management Information System In The Malaysian Public Sector

The successful implementation of information systems (IS) in organisations depends on the ability of the IS to assist users in performing their tasks by fulfilling their expectations and delivering the desired results. This paper describes the research on how well the Human Resource Management Information System (HRMIS) assists users in performing their tasks. The objective of this research is to identify gaps in HRMIS with regards to user needs in order to use HRMIS to assist them in performing their tasks and formulate recommendations to bridge the said gaps. Data was collected using a mixed methods approach of qualitative and quantitative methods. The quantitative data is being prepared for analysis and will be analysed using SPSS and AMOS. Content analysis was conducted on the open-ended responses which are being theme coded in SPSS for analysis. Content analysis will be conducted on the qualitative data upon its transcription and summarisation

Familial and socio-environmental predictors of overweight and obesity among primary school children in Selangor and Kuala Lumpur.

Introduction: A cross-sectional study was conducted to determine the familial and socio-environmental predictors of overweight and obesity among 1430, 9-12 year-old primary school children and their parents in Selangor and Kuala Lumpur. Methodology: Body weight and height were measured and body mass index was calculated. Modified Child Feeding (CFQ) and Determinants of Adolescent Social Well-being and Health (DASH) questionnaires were used to measure familial and socio-environmental factors. Results: A total of 17.9% of the children were overweight while 16.0% were obese. Positive relationships were found between child’s BMI and parent’s BMI (r = 0.129, p < 0.01), concern about child’s weight (r = 0.125, p < 0.01) and restriction (r = 0.057, p < 0.05) to unhealthy foods. However, negative relationships were found between child’s BMI with pressure to eat (r = -0.135, p < 0.01) and neighbourhood safety perception (r = -0.053, p < 0.05). The logistic regression analysis showed that being male (Exp(β) = 0.538; 95% CI = 0.421-0.687), higher parent’s BMI (Exp (β) = 1.055; 95% CI = 1.028-1.082), higher concern about child’s weight (Exp (β) = 1.082; 95% CI = 1.030-1.127), low pressure to eat (Exp (β) = 0.857; 95% CI = 0.801-0.916) and low perception of neighbourhood safety (Exp (β) = 0.951; 95% CI = 0.913-0.990) were significantly associated with increased risk of overweight. Conclusion: Parents should be the main target for education to modify children’s weight status. Further research should be carried out to understand the mechanism of influence of parents and the socio-environment on child’s health

EFFECT OF BIOCHAR RESIDUE, COMPOST, AND UREA COMBINATION ON GROWTH AND YIELD OF MAIZE (ZEA MAYS L.)

The objective of this study was to know the effect of biochar residue, compost and urea fertilization on growth and yield of maize.  The research was conducted at University Farm Ie Seum Research Station, Aceh Besar district, Aceh Province, Indonesia.  The experimental arranged in a randomized complate block non factorial design with four replications.  There were eight treatment combinations: P1 (without biochar residue + without compost + without urea), P2 (without biochar residue + without compost + urea), P3 (without biochar residue + compost + without urea), P4 (without biochar residue + compost + urea), P5 (biochar residue + without compost + without urea), P6 (biochar residue + without compost + urea), P7 (biochar residue + compost + without urea), P8 (biochar residue + compost + urea).  Based on the plant growth, biochar residue, compost, and urea fertilizer treatment did not significantly affect on plant height age of 30, 45 and 60 days after planting, leaf number aged 30, 45 and 60 days after planting, stem diameter ages 30, 45 and 60 days after planting. At the P7 treatment (biochar residue + compost + without urea) gave the best value but does not differ significantly with all treatments tested.  Based on plant yield, biochar residue, compost, and urea fertilizer treatment did not significantly affect on cornhusk ear length, cornhusk cob diameter, cornhusk cob weight, cob length without cornhusk, cob diameter without cornhusk, cob weight without cornhusk, and yield.  At P5 treatment (biochar residue + without compost + without urea) cornhusk cobs and P7 (biochar residue + compost + without urea) cornhusk cobs and without cornhusk provide the best value but does not differ significantly with all treatments tested.

Miniaturization of Resonator based on Moore Fractal

This paper presents the simulation and fabrication of miniaturized half wavelength resonator design using Moore fractal iteration technique. These resonators have been prepared for wireless application at a centre frequency of 2.45GHz using a substrate with dielectric constant of 2.2. The size and performance are compared with the conventional half wavelength open line resonator. It can be shown that the Moore fractal iteration technique able to reduce 46% of the size of conventional half wavelength resonator through first iteration, and 30% through second iteration while maintaining the resonance performance. The resonators have been fabricated using conventional printed circuit board facilities not specialized in microwave devices. However, the unloaded Q-value of the Moore structure generally much lower compared the open line type

EFFECT OF BIOCHAR RESIDUE, COMPOST, AND UREA COMBINATION ON GROWTH AND YIELD OF MAIZE (ZEA MAYS L.)

The objective of this study was to know the effect of biochar residue, compost and urea fertilization on growth and yield of maize.  The research was conducted at University Farm Ie Seum Research Station, Aceh Besar district, Aceh Province, Indonesia.  The experimental arranged in a randomized complate block non factorial design with four replications.  There were eight treatment combinations: P1 (without biochar residue + without compost + without urea), P2 (without biochar residue + without compost + urea), P3 (without biochar residue + compost + without urea), P4 (without biochar residue + compost + urea), P5 (biochar residue + without compost + without urea), P6 (biochar residue + without compost + urea), P7 (biochar residue + compost + without urea), P8 (biochar residue + compost + urea).  Based on the plant growth, biochar residue, compost, and urea fertilizer treatment did not significantly affect on plant height age of 30, 45 and 60 days after planting, leaf number aged 30, 45 and 60 days after planting, stem diameter ages 30, 45 and 60 days after planting. At the P7 treatment (biochar residue + compost + without urea) gave the best value but does not differ significantly with all treatments tested.  Based on plant yield, biochar residue, compost, and urea fertilizer treatment did not significantly affect on cornhusk ear length, cornhusk cob diameter, cornhusk cob weight, cob length without cornhusk, cob diameter without cornhusk, cob weight without cornhusk, and yield.  At P5 treatment (biochar residue + without compost + without urea) cornhusk cobs and P7 (biochar residue + compost + without urea) cornhusk cobs and without cornhusk provide the best value but does not differ significantly with all treatments tested.

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

The role of local orography on the development of a severe rainfall event over western Peninsular Malaysia: a case study

Severe rainfall events are common in western Peninsular Malaysia. They are usually short and intense, and occasionally cause flash floods and landslides. Forecasting these local events is difficult and understanding the mechanisms of the rainfall events is vital for the advancement of tropical weather forecasting. This study investigates the mechanisms responsible for a local heavy rainfall event on 2 May 2012 that caused flash floods and landslides using both observations and simulations with the limited-area high-resolution UK Met Office Unified Model (MetUM). Results suggest that previous day rainfalls over Peninsular Malaysia and Sumatra Island influenced the development of overnight rainfall over the Strait of Malacca by low-level flow convergence. Afternoon convection over the Titiwangsa mountains over Peninsular Malaysia then induced rainfall development and the combination of these two events influenced the development of severe convective storm over western Peninsular Malaysia. Additionally, anomalously strong low-level north-westerlies also contributed to this event. Sensitivity studies were carried out to investigate the influence of the local orography on this event. Flattened Peninsular Malaysia orography causes a lack of rainfall over the central part of Peninsular Malaysia and Sumatra Island and produces a weaker overnight rainfall over the Strait of Malacca. By removing Sumatra Island in the final experiment, the western and inland parts of Peninsular Malaysia would receive more rainfall, as this region is more influenced by the westerly wind from the Indian Ocean. These results suggest the importance of the interation between land masses, orography, low-level flow and the diurnal cycle on the development of heavy rainfall events