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Background: Alterations in metabolism of fuel molecules in diabetes increase blood glucose. This causes long-term complications in many organs. Currently, available treatment options to maintain glycemic control are accompanied by various side effects. Therefore, there is need to develop newer antihyperglycemic agents of plant origin with no side effects.Objective: This study aimed to evaluate the anti-hyperglycemic effects of aqueous extract of Lawsonia inermis leaf in diabetic rats.Methods: Thirty albino rats of an average weight 203.21 ± 6.57 g were randomly divided into six groups. Diabetes was induced through intraperitoneal administration of 150 mg/kg body weight of alloxan in all animal groups except one. Group A, non-diabetic rats, and group B, a diabetic control group were treated with 0.5 mL of normal saline while group C was treated with 50 mg/kg body weight metformin. Groups D, E and F were administered daily dose of 200, 400, and 600 mg/kg body weight of aqueous extract of Lawsonia inermis leaf (AELIL) respectively for a period of 3 weeks. Parameters monitored are fasting blood glucose using glucometer, body weight, serum chemistry and liver enzyme markers.Results: Administration of AELIL showed a significant (p < 0.05) reduction in fasting blood glucose of diabetic rats in a dose dependent manner. Also, administration of AELIL has no negative effect on the body weight, serum chemistry and liver marker enzymes of diabetic rats. The AELIL showed no observable toxicity.Conclusion: AELIL possesses antidiabetic activity and safe for consumption.

This paper analyses the impact of tie-lines connecting different areas of power system and the location of wind farms on the small signal stability of a power system. The analysis is conducted using Monte Carlo simulation via modal analysis. The random samples are generated from two-parameter Weibull distribution to obtain possible representation of wind speed needed for the generation of wind power using Latin hypercube sampling techniques. The sampling technique allows the usage of a small sample size, which helps in reducing the simulation cost without jeopardising the accuracy of the result. Different scenarios are created and the oscillatory modes are calculated for different operating condition. The changes in modal characteristic of the system due to different operating conditions are evaluated by observing the movement on the complex plane. The results of the modal analysis for each scenario are validated using time domain simulation. Some of the key results show that wind power can have either positive or negative impact on the oscillation of a power system depending on the location in which it is integrated into the power system. It is also found out that strengthening the weak tie-lines can greatly improve the inter-area mode. This paper is useful in the planning stage of wind power projects.

In the food and beverage industry, where growing, processing, packaging, distribution, storage, preparation, serving and disposing of food is the order of the day; energy consumption becomes an important input. Various energy models have been developed since the early 1970s, the period when energy caught the attention of policymakers due to the sudden price increase. Among the models are the index decomposition analysis (IDA), artificial neural network (ANN) and data envelopment analysis (DEA). The purpose of this study is to combine the strengths of these models, i.e., IDA, ANN and DEA, to allow biases in one model to offset biases in the other, so as to examine the effectiveness of energy management policies in a particular food and beverage industry. The integrated model applied to the food and beverage revealed that approximately 11% of energy consumed could be saved.

In view of the close association between energy and economic growth, South Africa's aspirations for higher growth, more energy is required; formulating a long-term economic development plan and implementing an energy strategy for a country /industry necessitates establishing the correct relationship between energy and the economy. As insufficient energy or a lack thereof is reported to be a major cause of social and economic poverty, it is very important to select a model to forecast the consumption of energy reasonably accurately. This study presents techniques based on the development of multilayer perceptron (MLP) and radial basis function (RBF) of artificial neural network (ANN) models, for calculating the energy consumption of South Africa's industrial sector between 1993 and 2000. The approach examines the energy consumption in relation to the gross domestic product. The results indicate a strong agreement between model predictions and observed values, since the mean absolute percentage error is below 5%. When performance indices are compared, the RBF-based model is a more accurate predictor than the MLP model.

Modelling and Analysis of Squirrel Cage Induction Motor with Leading Reactive Power Injection