Development of an Artificial Neural Network Model for Predicting Surface Water Level: Case of Modder River Catchment Area

Published ArticleWater is vital for life; however, water is a scarce natural resource that is under serious threat of depletion. South Africa and indeed the Free State is a water-scarce region, and facing growing challenges of delivering fresh and adequate water to the people. In order to effectively manage surface water, monitoring and predictions tools are required to inform decision makers on a real-time basis. Artificial Neural Networks (ANNs) have proven that they can be used to develop such prediction models and tools. This research makes use of experimentation, prototyping and case study to develop, identify and evaluate the ANN with best surface water level prediction capabilities. What ANN’s techniques and algorithms are the most suitable for predicting surface water levels given parameters such as water levels, precipitation, air temperature, wind speed, wind direction? How accurately will the ANNs developed predict surface water levels of the Modder River catchment area

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

Control of triceps surae stimulation based on shank orientation using a uniaxial gyroscope during gait

This article presents a stimulation control method using a uniaxial gyroscope measuring angular velocity of the shank in the sagittal plane, to control functional electrical stimulation of the triceps surae to improve push-off of stroke subjects during gait. The algorithm is triggered during each swing phase of gait when the angular velocity of the shank is relatively high. Subsequently, the start of the stance phase is detected by a change of sign of the gyroscope signal at approximately the same time as heel strike. Stimulation is triggered when the shank angle reaches a preset value since the beginning of stance. The change of angle is determined by integrating angular velocity from the moment of change of sign. The results show that the real-time reliability of stimulation control was at least 95% for four of the five stroke subjects tested, two of which were 100% reliable. For the remaining subject, the reliability was increased from 50% found during the experiment, to 99% during offline processing. Our conclusion is that a uniaxial gyroscope on the shank is a simple, more reliable alternative to the heel switch for the purpose of restoring push-off of stroke subjects during gait

Physical activity interventions to improve daily walking activity in cancer survivors

Background Cancer patients may benefit from physical exercise programs. It is unclear, however, how sustained levels of physical activity are best achieved in this population. A systematic review was performed to summarize the current evidence of the effect of physical activity interventions on daily walking activity enhancement in cancer survivors, and to review the literature for its methodological quality. Methods A search in Medline, PEDro and the Cochrane databases was performed for English literature citations (randomized controlled trials; `RCTs`). In a first step, one reviewer abstracted data from the included studies on patients, physical activity interventions and outcomes. Two independent reviewers reviewed the methodological quality of these studies. Data were pooled using random-effects calculations. Results Our search identified 201 citations. Five RCTs that reported changes in daily step activity over time were identified, and were reviewed for methodological quality and substantive results. The median score across studies for methodological quality based on the PEDro criteria was 8. These 5 RCTs evaluated 660 participants with a mean age of 53.6 (SD 4.2) years. The mean change in daily step activity for patients with a physical exercise intervention was 526 daily steps (SD 537), with a range from -92 to 1299 daily steps. The data of three studies reporting the effect of combined physical activity and counseling on daily walking activity in breast cancer survivors were pooled, however; the I2 was 79%, indicating statistical heterogeneity between the three trials. Conclusion The 5 RCTs reviewed were of good methodological quality. Together they suggest that combined physical activity and counseling improves daily step activity in (breast) cancer survivors. Studies that define a step goal appear to be more effective in improving daily walking activity than studies that do not do so. However, the current results should be interpreted with caution because of the observed clinical and statistical heterogeneity. Future studies are warranted to evaluate the effects of goal targeted physical activity, with or without counseling, on daily walking in various cancer populations

Should the surgeon or the general practitioner (GP) follow up patients after surgery for colon cancer? A randomized controlled trial protocol focusing on quality of life, cost-effectiveness and serious clinical events

This trial has been registered at ClinicalTrials.gov. The trial registration number is: NCT00572143.© 2008 Augestad et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited