Predicting noise-induced hearing loss (NIHL) in TNB workers using GDAM algorithm

Noise is a form of a pollutant that is terrorizing the occupational health experts for many decades due to its adverse side-effects on the workers in the industry. Noise�Induced Hearing Loss (NIHL) handicap is one out of many health hazards caused due to excessive exposure to high frequency noise emitted from the machines. A number of studies have been carried-out to find the significant factors involved in causing NIHL in industrial workers using Artificial Neural Networks (ANN). Despite providing useful information on hearing loss, these studies have neglected some important factors. The traditional Back-propagation Neural Network (BPNN) is a supervised Artificial Neural Networks (ANN) algorithm. It is widely used in solving many real time problems in world. But BPNN possesses a problem of slow convergence and network stagnancy. Previously, several modifications were suggested to improve the convergence rate of Gradient Descent Back-propagation algorithm such as careful selection of initial weights and biases, learning rate, momentum, network topology, activation function and ‘gain’ value in the activation function. This research proposed an algorithm for improving the current working performance of Back-propagation algorithm by adaptively changing the momentum value and at the same time keeping the ‘gain’ parameter fixed for all nodes in the neural network. The performance of the proposed method known as ‘Gradient Descent Method with Adaptive Momentum (GDAM)’ is compared with ‘Gradient Descent Method with Adaptive Gain (GDM-AG)’ (Nazri, 2007) and ‘Gradient Descent with Simple Momentum (GDM)’ by performing simulations on classification problems. The results show that GDAM is a better approach than previous methods with an accuracy ratio of 1.0 for classification problems like ix Thyroid disease, Heart disease, Breast Cancer, Pima Indian Diabetes, Wine Quality, Australian Credit-card approval problem and Mushroom problem. The efficiency of the proposed GDAM is further verified by means of simulations on Noise-Induced Hearing loss (NIHL) audiometric data obtained from Tenaga Nasional Berhad (TNB). The proposed GDAM shows improved prediction results on both ears and will be helpful in improving the declining health condition of industrial workers in Malaysia. At present, only few studies have emerged to predict NIHL using ANN but have failed to achieve high accuracy. The achievements made by GDAM has paved way for indicating NIHL in workers before it becomes severe and cripples him or her for life. GDAM is also helpful in educating the blue collared employees to avoid noisy environments and remedies against exposure to excessive noise can be taken in the future to prevent hearing damage

Noise-Induced Hearing Loss (NIHL) Prediction in Humans Using a Modified Back Propagation Neural Network

Noise-Induced Hearing Loss (NIHL) has become a major source of health problem in industrial workers due to continuous exposure to high frequency sounds emitting from the machines. In the past, several studies have been carried-out to identify NIHL industrial workers. Unfortunately, these studies neglected some important factors that directly affect hearing ability in human. Artificial Neural Network (ANN) provides very effective way to predict hearing loss in humans. However, the training process for an ANN required the designers to arbitrarily select parameters such as network topology, initial weights and biases, learning rate value, the activation function, value for gain in activation function and momentum. An improper choice of any of these parameters can result in slow convergence or even network paralysis, where the training process comes to a standstill or get stuck at local minima. Therefore, this current study focuses on proposing a new framework on using Gradient Descent Back Propagation Neural Network model with an improvement on the momentum value to identify the important factors that directly affect the hearing ability of industrial workers. Results from the prediction will be used in determining the environmental health hazards which affect the workers health

Estimation of Surface Heat Flux and Surface Temperature during Inverse Heat Conduction under Varying Spray Parameters and Sample Initial Temperature

An experimental study was carried out to investigate the effects of inlet pressure, sample thickness, initial sample temperature, and temperature sensor location on the surface heat flux, surface temperature, and surface ultrafast cooling rate using stainless steel samples of diameter 27 mm and thickness (mm) 8.5, 13, 17.5, and 22, respectively. Inlet pressure was varied from 0.2 MPa to 1.8 MPa, while sample initial temperature varied from 600°C to 900°C. Beck’s sequential function specification method was utilized to estimate surface heat flux and surface temperature. Inlet pressure has a positive effect on surface heat flux (SHF) within a critical value of pressure. Thickness of the sample affects the maximum achieved SHF negatively. Surface heat flux as high as 0.4024 MW/m2 was estimated for a thickness of 8.5 mm. Insulation effects of vapor film become apparent in the sample initial temperature range of 900°C causing reduction in surface heat flux and cooling rate of the sample. A sensor location near to quenched surface is found to be a better choice to visualize the effects of spray parameters on surface heat flux and surface temperature. Cooling rate showed a profound increase for an inlet pressure of 0.8 MPa

Effect of Empagliflozin On Liver Enzymes of Patients In Non-Alcoholic Steatohepatitis In Type 2 Diabetes Mellitus

OBJECTIVES To assess the effect of 10 mg and 25 mg once daily Empagliflozin on liver enzymes of patients of non-alcoholic steatohepatitis in patients of type 2 diabetes mellitus (T2DM). METHODOLOGY The study design was Quasi Experimental. Thirty three adult patients of  Type 2 diabetes mellitus (T2DM) who were already on 2000 mg of Metformin and 100 mg of Sitagliptin and were having suboptimal glycemic control (HBA1C > 7% <12%), had elevated Alanine Transaminase (ALT) levels and had ultrasonographic features consistent with non-alcoholic fatty liver disease (NAFLD) were divided into three groups, one group receiving 10 mg Empagliglozin as add-on treatment (Group A), the second group receiving 25 mg of Empagliflozin (Group B) as an additional treatment, and the third group continued with previous medications without any additional treatment (Group C). HbA1C levels and ALT levels of all the three groups were taken at baseline and at 12 weeks. RESULTS Total patients and their mean ages in group A, B and C were 10, 12 and 11 and 52.40 ±4.24 years, 52.42 ± 5.27 years and 52.34 ± 4.37 years, respectively. There was a statistically significant (p > 0.05) decrease mean ALT levels in Group A pre-treatment and 12 weeks post-treatment. Similarly, there was a statistically significant (p > 0.05) decrease mean ALT levels in Group B pre-treatment and 12 weeks post-treatment.CONCLUSION Empagliflozin in both 10mg and 25 mg once daily doses cause statistically significant reduction in ALT levels in patients with NASH associated with T2D

Asymmetric Flow-Performance Relationship: Case of Chinese Equity Funds

We investigate the relationship of fund flow and fund performance by using the sample of 557 Chinese equity mutual funds for the period of 11 years. We apply fund fixed effect regression model on unbalanced panel data and find that relationship between fund flow and fund performance is positive. Like previous studies, our findings also exhibit the asymmetric flow-performance relationship which implies that investors' response is more sensitive to good past performance as compared to bad past performance. Furthermore, size and age of fund weaken the flow-performance relationship. Keywords: Fund Flow, Mutual funds, China, Flow-Performance Relationship JEL Classifications: G23, L1

Status of Phosphorous in Soil and Plants of Apple Orchads in Quetta Valley

The study was planned to assess the phosphorous status of apple orchards around Quetta valley through soil and plant analysis. Two hundred soil samples were acquired from 40 different locations of 05 orchards at  0-15, 15-30, 30-45, 45-60, 60- 75 and 75-90 cm depths. The soil samples were analyzed for available phosphorous by AB-DTPA method. At the same time forty apple trees were selected for leaves samples and analyzed for plant phosphorous content by Rhoades, 1982 .AB-DTPA phosphorous in soil showed a sizeable variation ranging from 0.24 to 14.46 mg kg-1. Whereby plant analysis registered minimum phosphorous content 0.06% and the maximum phosphorous content 0.47% in apple plant leaves which confirms phosphorous concentration found deficient in apple orchards of Quetta valle