Frequently there are disasters all over the world-fires, earthquakes, or even some unexpected shocking catastrophes. Hence people injured, or even died. Lifesaving actions begin with the initiation of the chain of survival. With every minute that passes without medical action being taken, the probability of being able to save the patients life decreases by ten percent. After 10 min there is normally no chance of resuscitation being successful. First aid is emergency treatment given before regular medical aid can be obtained. And it is a concept of first hands-on measures performed in a medical emergency by laypersons. The major aim of this study is to develop an easy-feasible cervical collar, for facilitating and accelerating implementation of first aid especially in case of collective injuries. The developed device is different from the cervical collars which are used to treat the neck pain. In the present study, the heartbeat is obtained by detecting pulse with the stethoscope that is a part of the developed device and fixed on the carorid artery. The obtained heartbeat signal has been processed by the electronic control circuit and the used LED has given light according to the patient's life signal. Although there are some disadvantages of the developed system, the precautions for these cases have been taken and the system has been tried to design in order to operate sensibly.
Short term load forecasting is a subject about estimating future electricity consumption for a time interval from one hour to one week and it has a vital importance for the operation of a power system and smart grids. This process is mandatory for distribution companies and big electricity consumers, especially in liberalized energy markets. Electricity generation plans are made according to the amount of electricity consumption forecasts. If the forecast is overestimated, it leads to the start-up of too many units supplying an unnecessary level of reserve, therefore the production cost is increased. On the contrary if the forecast is underestimated, it may result in a risky operation and consequently power outages can occur at the power system. In this study, a hybrid method based on the combination of Artificial Bee Colony (ABC) and Artificial Neural Network (ANN) is developed for short term load forecasting. ABC algorithm is used in ANN learning process and it optimizes the neuron connections weights of ANN. Historical load, temperature difference and season are selected as model inputs. While three years hourly data is selected as training data, one year hourly data is selected as testing data. The results show that the application of this hybrid system produce forecast values close to the actual values.
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