生物工程与生物医学杂志

In this paper, we introduce a novel cycle identification algorithm using MATLAB programming to automatically identify cycles in tidal breathing signals. The algorithm was designed in four steps using filtering, derivation, and other signal processing techniques. To verify the accuracy of the proposed algorithm, its results were compared with those of cycles identified manually by a human coder. Simulations results showed that despite the complexity of respiratory signals, the proposed algorithm could identify cycles more accurately than the human coder. This algorithm could serve as an important first step toward timely identification and coding for more complex respiratory signals, such as those underlying speech productions.

In this work, Kluyveromyces fragilis yeast was used for bio-ethanol production from cheese-whey in batch fermentation. The present study consisted of two steps: The first was a central composite design (CCD) for evaluating of important factors including: pH, initial lactose concentration (L), yeast cells concentrations (Y) and temperature (T). In order to optimize the fermentation process, response surface methodology (RSM) was used in this stage. The best operating conditions were found to be pH = 5.3, L = 41.8 g/l, Y = 0.57 g/l and T= 30.8°C. The second step was to determine the effect of aeration rate on the fermentation process in an airlift bioreactor. The best conditions were the aeration rate of 0.4 vvm with 89.28% of ethanol production yield. In this research, the concentrated cheese whey was also used for obtaining a bio-ethanol fermentation product.

Bilateral movement training based on robot-aided rehabilitation systems has been attracting a lot of attention as a post-stroke motor rehabilitation protocol. However, the critical training parameters that underlie the efficiency of bilateral movement have not been clarified. The primary question for planning of bilateral movement training is how the upper extremities interact with each other when function in one of the limbs is less than normal one. The effects of different conditions which were imposed on the unimpaired upper extremity were investigated to find exact therapeutic conditions for planning more appropriate bilateral movement training. Active/passive, loaded/non-loaded, and unimanual/bimanual movements were used as the experimental conditions. Twenty subjects were randomly assigned to one of four groups, namely the passive group (PG), the active non-load group (ANLG), active load group (ALG), and the control group (CG) and were asked to perform tasks with their left upper extremity with respect to the conditions. To carry out the experiments with healthy subjects, we use a robotic force field paradigm to impose a virtual impairment on the right upper extremity of the all subjects. After subject adapted to the robotic force field, all subject conducted the aftereffect test which consist of a bimanual movement task while the CG performed a unimanul movement task. Here we assume that, based on the bilateral transfer aspect, the recovery time from the adaptation to the robotic force field is varied by the conditions of left upper extremity in bimanual movement task. By comparing the recovery time from adaptation in each condition, we found the exact condition for planning of effective bilateral movement training. The comparison results revealed that the active loaded group showed the recovery time from adaptation was faster than another groups.

In this paper, we introduce a novel cycle identification algorithm using MATLAB programming to automatically identify cycles in tidal breathing signals. The algorithm was designed in four steps using filtering, derivation, and other signal processing techniques. To verify the accuracy of the proposed algorithm, its results were compared with those of cycles identified manually by a human coder. Simulations results showed that despite the complexity of respiratory signals, the proposed algorithm could identify cycles more accurately than the human coder. This algorithm could serve as an important first step toward timely identification and coding for more complex respiratory signals, such as those underlying speech productions.