Modulation of electroencephalograph activity by manual acupuncture stimulation in healthy subjects:An autoregressive spectral analysis

To investigate whether and how manual acupuncture（MA） modulates brain activities,we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph（EEG） signals in healthy subjects.We adopt the autoregressive（AR） Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta（0 Hz-4 Hz）,theta（4 Hz-8 Hz）,alpha（8 Hz-13 Hz）,and beta（13 Hz-30 Hz） bands.Our results show that MA at ST36 can significantly increase the EEG slow wave relative power（delta band） and reduce the fast wave relative powers（alpha and beta bands）,while there are no statistical differences in theta band relative power between different acupuncture states.In order to quantify the ratio of slow to fast wave EEG activity,we compute the power ratio index.It is found that the MA can significantly increase the power ratio index,especially in frontal and central lobes.All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture.The proposed quantitative method of acupuncture signals may be further used to make MA more standardized.

Predicting Electric Energy Consumption for a Jerky Enterprise

Wholesale and retail markets for electricity and power require consumers to forecast electricity consumption at different time intervals. The study aims to increase economic efficiency of the enterprise through the introduction of algorithm for forecasting electric energy consumption unchanged in technological process. Qualitative forecast allows you to essentially reduce costs of electrical energy, because power cannot be stockpiled. Therefore, when buying excess electrical power, costs can increase either by selling it on the balancing energy market or by maintaining reserve capacity. If the purchased power is insufficient, the costs increase is due to the purchase of additional capacity. This paper illustrates three methods of forecasting electric energy consumption: autoregressive integrated moving average method, artificial neural networks and classification and regression trees. Actual data from consuming of electrical energy was used to make day, week and month ahead prediction. The prediction effect of prediction model was proved in Statistica simulation environment. Analysis of estimation of the economic efficiency of prediction methods demonstrated that the use of the artificial neural networks method for short-term forecast allowed reducing the cost of electricity more efficiently. However, for mid- range predictions, the classification and regression tree was the most efficient method for a Jerky Enterprise. The results indicate that calculation error reduction allows decreases expenses for the purchase of electric energy.