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The estrous cycle is a potent modulator of neuron physiology. In rodents,in vivoventral tegmental area (VTA) dopamine (DA) activity has been shown to fluctuate across the estrous cycle. While the behavioral effect of fluctuating sex steroids on the reward circuit is well studied in response to drugs of abuse, few studies have focused on the molecular adaptations in the context of stress and motivated social behaviors. We hypothesized that estradiol fluctuations across the estrous cycle acts upon the dopaminergic activity of the VTA to alter excitability and stress response. We used whole-cell slice electrophysiology of VTA DA neurons in naturally cycling, adult female C57BL/6J mice to characterize the effects of the estrous cycle and the role of 17β-estradiol on neuronal activity. We show that the estrous phase alters the effect of 17β-estradiol on excitability in the VTA. Behaviorally, the estrous phase during a series of acute variable social stressors modulates subsequent reward related behaviors. Pharmacological inhibition of estrogen receptors in the VTA prior to stress during diestrus mimics the stress susceptibility found during estrus while increased potassium channel activity in the VTA prior to stress reverses stress susceptibility found during estrus as assessed by social interaction behavior. This study identifies one possible potassium channel mechanism underlying the increased DA activity during estrus and reveals estrogen dependent changes in neuronal function. Our findings demonstrate that the estrous cycle and estrogen signaling changes the physiology of DA neurons resulting in behavioral differences when the reward circuit is challenged with stress.SIGNIFICANCE STATEMENT:The activity of the ventral tegmental area encodes signals of stress and reward. Dopaminergic activity has been found to be regulated by both local synaptic inputs as well as inputs from other brain regions. Here, we provide evidence that cycling sex steroids also plays are role in modulating stress sensitivity of dopaminergic reward behavior. Specifically, we reveal a correlation of ionic activity with estrous phase, which influences the behavioral response to stress. These findings shed new light how estrous cycle may influence dopaminergic activity primarily during times of stress perturbation.

Energy hole problem is considered one of the most severe threats in wireless sensor networks. In this paper the idea of exploiting sink mobility for the purpose of culling the energy hole problem in hierarchical large-scale wireless sensor networks based on bees algorithm is presented. In the proposed scheme, a mobile sink equipped with a powerful transceiver and battery, traverses the entire field, and periodically gathers data from network cluster heads. The mobile sink follows an adaptive gathering strategy resilient to both connected and disconnected networks. The proposed gathering strategy geared to eliminate multihop relays required by all cluster heads to reach the mobile sink, balancing the traffic load across all network heads, meanwhile, reducing the loss that data may incur due to buffer overflow. Furthermore, enabling the mobile sink to navigate safely within cluttered and uncluttered fields augments the proposed gathering strategy. Extensive simulations are conducted in order to validate the effectiveness of the proposed strategy. The achieved results show an improvement in overall system performance compared to other mobility strategies.

The conventional methods of observer poles placement in sensor fault detection usually adopt the trial-and-error methods. These methods cannot achieve global optimal performance because of their fixed poles placement and it leads to an observer with constant parameters, which could be reducing the system performance. Therefore, this paper proposes a fuzzy-based observer tuning method to optimize and adapt the selection of poles locations to determine the optimal gains of the observer, and it is experimentally applied to a composite sensor fault detection. Fuzzy logic is a promising method that could overcome the trial-and-error method challenges by introducing better adaptation and system robustness. The proposed observer structure includes adaptive tuning corresponding to an unknown input. Utilizing self-tuning for the observer correction stage, the gain is going to be updated online using the proposed fuzzy adaptive poles placement (FAPP) system. This paper validated the system simulation by implementing fault detection algorithms by using a real-time embedded observer-based system. The experimental results demonstrate the effectiveness of the proposed fuzzy-based observer schemes at detecting sensor faults in the Brushless DC (BLDC) motors, with significantly better performance than conventional counterparts' methodologies. The experiments indicate that the average estimation error is 0.146, which less by 43.8% than was obtained for high levels of noise and disturbances compared with the traditional Luenberger observer approach.

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