To understand the effects of arecoline administration on the muscarinic cholinergic signaling pathway, rats were injected with arecoline, 10 mg/kg i.p., and the carbachol‐stimulated phosphoinositide breakdown in rat brain cortical slices was examined. In vivo administration of arecoline resulted in inhibition of carbachol‐stimulated phosphoinositide turnover in rat brain cortical slices. Arecoline was a partial agonist with peak effects of 30% of the maximum as obtained with carbachol. Coaddition of arecoline inhibited the carbachol‐stimulated phosphoinositide breakdown. Pretreatment of rat brain cortical slices with arecoline in vitro resulted in a dose‐dependent inhibition of carbachol‐stimulated [3H]inositol monophosphate accumulation. The inhibition occurred rapidly, with half‐maximal inhibition occurring at 15 min and maximal inhibition achieved within 60 min. The inhibition of phosphoinositide breakdown was recovered 1 h after arecoline was removed. When synaptoneurosomes were used for the ligand binding studies, arecoline pretreatment was found to have decreased the maximal ligand binding (Bmax) without inducing any marked change in binding affinity (KD). The influence could be recovered by incubating the synaptoneurosomes in the absence of arecoline for 2 h. Taken together, these data suggest that the underlying mechanism by which phosphoinositide turnover is inhibited is arecoline‐induced receptor sequestration.

In the paper, design and Implementation of cloud-dust based intelligent system is proposed. For achieving applications of intelligent system, such as records, surveillance, assessments, predictions, diagnosis, prescription, scheduling and fool-proofing checks, an architecture named Cloud-Dust is developed. The intelligent system is separated into the cloud system and the dust system. The dust system contains (1) Wireless sensors network (2) Features extraction circuits (3) Intelligent computing circuits (4) Embedded system. It can play a role as real-time preprocessor very well, just like an intelligent agent. However, the cloud system contains (1) Cloud database (2) Intelligent computing engine (3) Ubiquitous human-machine-interface. It can flexibly use computing resources and integrate information from many different dust systems. By the experiments, we can find the advantages of the cloud-dust based intelligent system. It meets the both needs of real-time and integration for intelligent systems. So it is necessary to develop the cloud-dust based system for design and implementation of the intelligent system.

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In the paper, a cloud-dust based intelligent maximum power analysis system for photovoltaic is proposed. In order to resolve NP problem for photovoltaic, factors of photovoltaic are integrated to cloud-dust based intelligent maximum power analysis system for computing. This study is the development of the maximum power analysis system for photovoltaic, to improve the solar panels effects of the different region and enable them to get maximum efficiency of the power generation. The design methodology of this study includes: (1) The monitoring and control Module (2) The prediction and evaluation module (3) The performance diagnosis module (4) The maintenance prescription module. At last, we can find the advantages of the cloud-dust based intelligent maximum power analysis system for photovoltaic. It increases overall competitive performance of products, reduces cost of products and consummation rates of human resources.

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