Diabetes mellitus and its main complication, nephropathy, ajjbcts the economic wellbeing and quality of Iife of the sufferers and the population. A matched case control study was conducted in September 1998 to investigate the factors involved with nephropathy such as diabetic control, smoking, hypertension, familv history of diabetes and diabetic duration. Respondents were classyied based on the presence of microalbuminuria or macroalbuminuria, Seventy-two pairs of case and control were studied Duration of diabetes Q2 = 0.005), presence of lethargy and weakness prior to diabetes diagnosis @7 = 0.019), duration of smoking @7 = 0.014), duration of hypertension @2: 0.000), systolic hypertension Qu= 0e 025), uncontrolled diabetes with poor HbA1c level (v= 0.02Q and lack of diabetes knowledge Q2 = 0.037) were jbctors which related signyicantlv to nephropathy by univariate anahrsis. In multivariate anahrsis, systolic hypertension (p = 0.0015), lack of diabetes knowledge (17 = 0.0197), presence of lethargy symptom Q7 = 0.0027), prolonged diabetic duration @ = 0.0301) and higher body mass indices (p = 0. 0213) were predictors to diabetic nephropathy.
The performance of a chemical process plant can gradually degrade due to deterioration of the process equipment and unpermitted deviation of the characteristic variables of the system. Hence, advanced supervision is required for early detection, isolation and correction of abnormal conditions. This work presents the use of an adaptive neuro-fuzzy inference system (ANFIS) for online fault diagnosis of a gas-phase polypropylene production process with emphasis on fast and accurate diagnosis, multiple fault identification and adaptability. The most influential inputs are selected from the raw measured data sets and fed to multiple ANFIS classifiers to identify faults occurring in the process, eliminating the requirement of a detailed process model. Simulation results illustrated that the proposed method effectively diagnosed different fault types and severities, and that it has a better performance compared to a conventional multivariate statistical approach based on principal component analysis (PCA). The proposed method is shown to be simple to apply, robust to measurement noise and able to rapidly discriminate between multiple faults occurring simultaneously. This method is applicable for plant-wide monitoring and can serve as an early warning system to identify process upsets that could threaten the process operation ahead of time.
Survival after diagnosis of cancer is one of the major outcome measurements and a key criterion for assessing quality of cancer control related to both the preventive and the therapeutic level. The purpose of this study was to determine the 8-year survival time in Malaysia based on socio-demographic and clinical characteristics. A retrospective study of 472 Malaysian women with breast cancer from the Medical Record Department at University Kebangsaan Malaysia Medical Centre (UKMMC) was therefore performed with survival analysis carried out using the Kaplan-Meier with log-rank test for univariate analysis and Cox-regression for multivariate analysis. Women who had cancer or family history of cancer had a longer 8-year survival time (p = 0.008) compared with others who did not have such a history. Tamoxifen use, positive oestrogen receptor status, and race were prognostic indicators for 8-year survival time (p = 0.036, p = 0.018, p = 0.053, respectively) in univariate analysis. Multivariate analysis showed that being Malays and having no family history of cancer were independent prognostic factors for shorter survival time (p = 0.008, p = 0.012, respectively). In conclusion, being Chinese and having a family history of cancer are predictors of longer survival among the Malaysian breast cancer women.
Extensive application of metal powder, particularly in nanosize could potentially lead to catastrophic dust explosion, due to their pyrophoric behavior, ignition sensitivity, and explosivity. To assess the appropriate measures preventing accidental metal dust explosions, it is vital to understand the physicochemical properties of the metal dust and their kinetic mechanism. In this work, explosion severity of aluminum and silver powder, which can be encountered in a passivated emitter and rear contact (PERC) solar cell, was explored in a 0.0012 m3 cylindrical vessel, by varying the particle size and powder concentration. The P max and dP/dt max values of metal powder were demonstrated to increase with decreasing particle size. Additionally, it was found that the explosion severity of silver powder was lower than that of aluminum powder due to the more apparent agglomeration effect of silver particles. The reduction on the specific surface area attributed to the particles' agglomeration affects the oxidation reaction of the metal powder, as illustrated in the thermogravimetric (TG) curves. A sluggish oxidation reaction was demonstrated in the TG curve of silver powder, which is contradicted with aluminum powder. From the X-ray photoelectron spectroscopy (XPS) analysis, it is inferred that silver powder exhibited two reactions in which the dominant reaction produced Ag and the other reaction formed Ag2O. Meanwhile, for aluminum powder, explosion products only comprise Al2O3.