Association of Streptococcus bovis with colorectal carcinoma

Background: - This study was carried out to investigate the ability of Streptococcus bovis to colonise colorectal cancer. Patients and Methods: - A total of 106 outpatients were subjected for colonscopy. Carcinoma biopsies from patients with colorectal cancer tissue from patient with polyps and normal mucosa stool and blood from all patient and controls were cultured and identified for S.bovis. Results: - The histopatholgical findings confirmed that 38 patients had colorectal carninoma, 27 patients with benign polyps and 41 with normal colonic mucosa. The faecal carriage rate of S.bovis was 15(39.5%) in patient with colorected cancer, 5(18.5%) in patients with polyp and 7(17.1%) in control. Conclusion: - Faecal colonization by Streptococcus bovis in colorectal cancer patient was higher than in control healthy people and patients with polyp

An Adaptive Backpropagation Algorithm for Long Term Electricity Load Forecasting

Artificial Neural Networks (ANNs) have been widely used to determine future demand for power in the short, medium, and long terms. However, research has identified that ANNs could cause inaccurate predictions of load when used for long-term forecasting. This inaccuracy is attributed to insufficient training data and increased accumulated errors, especially in long-term estimations. This study develops an improved ANN model with an Adaptive Backpropagation Algorithm (ABPA) for best practice in the forecasting long-term load demand of electricity. The ABPA includes proposing new forecasting formulations that adjust/adapt forecast values, so it takes into consideration the deviation between trained and future input datasets' different behaviours. The architecture of the Multi-Layer Perceptron (MLP) model, along with its traditional Backpropagation Algorithm (BPA), is used as a baseline for the proposed development. The forecasting formula is further improved by introducing adjustment factors to smooth out behavioural differences between the trained and new/future datasets. A computational study based on actual monthly electricity consumption inputs from 2011 to 2020, provided by the Iraqi Ministry of Electricity, is conducted to verify the proposed adaptive algorithm's performance. Different types of energy consumption and the electricity cut period (unsatisfied demand) factor are also considered in this study as vital factors. The developed ANN model, including its proposed ABPA, is then compared with traditional and popular prediction techniques such as regression and other advanced machine learning approaches, including Recurrent Neural Networks (RNNs), to justify its superiority amongst them. The results reveal that the most accurate long-term forecasts with the minimum Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values of (1.195.650) and (0.045), respectively, are successfully achieved by applying the proposed ABPA. It can be concluded that the proposed ABPA, including the adjustment factor, enables traditional ANN techniques to be efficiently used for long-term forecasting of electricity load demand